{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RSL_fcry4DAQ"
      },
      "source": [
        "# **Spotlight Kampala**\n",
        "# Analysis for report \"Advancing Electric Cooking Transitions in Informal Settlements\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zvNNWHs24MB3"
      },
      "source": [
        "# Import libraries, data and pre-analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDdUUG7S4OD1"
      },
      "source": [
        "### Import necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SBLgCCU_3_Cz"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from cycler import cycler\n",
        "import numpy as np\n",
        "import string\n",
        "import matplotlib.patches as mpatches\n",
        "import matplotlib\n",
        "from matplotlib import rcParams\n",
        "import seaborn as sns\n",
        "from google.colab import drive\n",
        "import matplotlib.colors\n",
        "import matplotlib.text as mtext\n",
        "from matplotlib.ticker import PercentFormatter\n",
        "from matplotlib.gridspec import GridSpec\n",
        "from IPython.display import display\n",
        "import re\n",
        "from  matplotlib.colors import LinearSegmentedColormap, to_rgba, rgb2hex\n",
        "import scipy.stats as stats\n",
        "from matplotlib.ticker import FuncFormatter\n",
        "from matplotlib.legend_handler import HandlerTuple\n",
        "import matplotlib.ticker as mtick\n",
        "import os\n",
        "from datetime import datetime\n",
        "import plotly.graph_objects as go\n",
        "import matplotlib.gridspec as gridspec\n",
        "import matplotlib.lines as mlines\n",
        "import matplotlib.ticker as ticker\n",
        "from matplotlib.patches import Rectangle"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sB_1rO3rj7AY"
      },
      "source": [
        "### Set global formatting parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p1c5GRWWjeR8"
      },
      "outputs": [],
      "source": [
        "# Set global formatting variables\n",
        "rcParams[\"font.sans-serif\"] = [\"DejaVu Sans\"]\n",
        "rcParams[\"font.family\"] = \"sans-serif\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8HItUeSTkC8Q"
      },
      "source": [
        "### Define file path and read in survey data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IMjP5lcG4xRx",
        "outputId": "3948a57b-7e2f-49d5-927f-264f836f05c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "djziBFVspbTc"
      },
      "outputs": [],
      "source": [
        "def combine_files(folder_path):\n",
        "    df_list = []\n",
        "    base_columns = None  # Track the column names from the first file\n",
        "\n",
        "    for file in os.listdir(folder_path):\n",
        "        if not file.endswith('.csv'):\n",
        "            print(f\"❌ Skipping non-CSV file: {file}\")\n",
        "            continue\n",
        "\n",
        "        full_path = os.path.join(folder_path, file)\n",
        "\n",
        "        if os.stat(full_path).st_size == 0:\n",
        "            print(f\"⚠️ Skipped empty file: {file}\")\n",
        "            continue\n",
        "\n",
        "        try:\n",
        "            device_id = os.path.basename(file).replace(\".csv\", \"\")\n",
        "\n",
        "            # Read a sample to detect duplicated headers\n",
        "            df_sample = pd.read_csv(full_path, nrows=2, engine=\"python\", encoding=\"utf-8\", on_bad_lines=\"skip\")\n",
        "            df = pd.read_csv(full_path, engine=\"python\", encoding=\"utf-8\", on_bad_lines=\"skip\")\n",
        "\n",
        "            if df_sample.columns.tolist() == df.iloc[0].tolist():\n",
        "                df = pd.read_csv(full_path, skiprows=1, engine=\"python\", encoding=\"utf-8\", on_bad_lines=\"skip\")\n",
        "\n",
        "            # Set column headers based on the first successfully loaded file\n",
        "            if base_columns is None:\n",
        "                base_columns = df.columns.tolist()\n",
        "            else:\n",
        "                df.columns = base_columns[:len(df.columns)] + [f'Extra_{i}' for i in range(len(df.columns) - len(base_columns))]\n",
        "\n",
        "            # Add DEVICE ID\n",
        "            df[\"DEVICE ID\"] = device_id\n",
        "\n",
        "            df_list.append(df)\n",
        "\n",
        "        except Exception as e:\n",
        "            print(f\"❌ Error processing {file}: {e}\")\n",
        "\n",
        "    combined_df = pd.concat(df_list, ignore_index=True)\n",
        "\n",
        "    print(\"✅ Files processed:\", combined_df[\"DEVICE ID\"].unique())\n",
        "    return combined_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6FfAoko14tq5",
        "outputId": "968415e1-867c-442c-cf4f-9487ac18f3c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "❌ Skipping non-CSV file: Inventory of nline sensors\n",
            "❌ Skipping non-CSV file: Namuwongo_Analysis_Final_COPY.ipynb\n",
            "❌ Skipping non-CSV file: Participant data.xlsx\n",
            "✅ Files processed: ['EM_073_2023-06-27' 'EM_085_2023-06-27' 'EM_066_2023-06-17'\n",
            " 'EM_064_2023-06-17' 'EM_069_2023-06-27' 'EM_084_2023-06-27'\n",
            " 'EM_094_2023-07-01' 'EM_092_2023-06-27' 'EM_089_2023-06-27'\n",
            " 'EM_070_2023-06-17' 'EM_099_2023-06-27' 'EM_083_2023-06-27'\n",
            " 'EM_088_2023-06-27' 'EM_095_2023-06-27' 'EM_087_2023-06-27'\n",
            " 'EM_093_2023-06-27' 'EM_098_2023-06-27' 'EM_074_2023-07-01'\n",
            " 'EM_071_2023-06-17' 'EM_101_2023-06-17' 'EM_103_2023-06-27'\n",
            " 'EM_091_2023-06-27' 'EM_075_2023-06-17' 'EM_100_2023-06-17'\n",
            " '060223-1956-EM72_v2.1' '061423-0850-EM104_v2.1' '061023-2131-EM105_v2.1']\n"
          ]
        }
      ],
      "source": [
        "# Paths to datasets\n",
        "path_gen_survey = '/content/drive/My Drive/Spotlight Kampala/Phase 1 Fieldwork/Surveys/'\n",
        "path_cooking_survey = '/content/drive/My Drive/Spotlight Kampala/Phase 1 Fieldwork/e-Cooking/Cooking Surveys/'\n",
        "path_wiring_inspection = '/content/drive/My Drive/Spotlight Kampala/Phase 1 Fieldwork/e-Cooking/Wiring Inspections/Data/'\n",
        "path_consumption_monitoring_data = '/content/drive/My Drive/Spotlight Kampala/Phase 1 Fieldwork/e-Cooking/Consumption Monitoring/A2EI sensors/'\n",
        "path_consumption_monitoring_data_kosko = '/content/drive/My Drive/Spotlight Kampala/Phase 1 Fieldwork/e-Cooking/Consumption Monitoring/Kosko sensors/'\n",
        "path_consumption_monitoring_data_pre_survey = '/content/drive/My Drive/Spotlight Kampala/Phase 1 Fieldwork/e-Cooking/Consumption Monitoring/'\n",
        "path_remote_monitoring_data_pre_survey = '/content/drive/My Drive/Spotlight Kampala/Phase 1 Fieldwork/Remote Monitoring/'\n",
        "fig_path = '/content/drive/My Drive/Spotlight Kampala/Outputs/Special Report on Barriers to Electric Cooking/Graphics/'\n",
        "\n",
        "# Function to read all sheets from an Excel file into a dictionary of DataFrames\n",
        "def read_all_sheets(file_path):\n",
        "    sheets = pd.read_excel(file_path, sheet_name=None)\n",
        "    if len(sheets) == 1:\n",
        "        # If only one sheet, return the DataFrame directly\n",
        "        return list(sheets.values())[0]\n",
        "    return sheets\n",
        "\n",
        "# Reading general survey\n",
        "df_gen_survey = read_all_sheets(path_gen_survey + 'Spotlight Kampala Final Survey Dataset.xlsx')\n",
        "\n",
        "# Reading e-cooking survey\n",
        "df_cooking_survey = read_all_sheets(path_cooking_survey + 'Spotlight Kampala Cooking Survey Final Dataset.xlsx')\n",
        "\n",
        "# Reading wiring inspection\n",
        "df_wiring_inspection = read_all_sheets(path_wiring_inspection + 'Wiring Inspection Final Dataset.xlsx')\n",
        "\n",
        "# Reading consumption monitoring data from A2EI sensors\n",
        "df_consumption_monitoring_a2ei = pd.read_csv(path_consumption_monitoring_data + 'ug_informal_30 all raw_data merged and filled.csv')\n",
        "df_consumption_monitoring_a2ei_processed = pd.read_csv(path_consumption_monitoring_data + 'ug_informal_30 events_list_f_f.csv')\n",
        "\n",
        "# Reading consumption monitoring data from Kosko sensors\n",
        "df_consumption_monitoring_kosko = combine_files(path_consumption_monitoring_data_kosko)\n",
        "\n",
        "# Reading consumption monitoring pre-deployment survey\n",
        "df_consumption_monitoring_pre_survey = read_all_sheets(path_consumption_monitoring_data_pre_survey + 'Consumption Monitoring Pre-Deployment Survey Results.xlsx')\n",
        "\n",
        "# Reading consumption monitoring participant-appliance mapping\n",
        "df_consumption_monitoring_mapping = pd.read_csv(path_consumption_monitoring_data_pre_survey + 'Participant Appliance Mapping.csv')\n",
        "\n",
        "# Reading remote power quality monitoring pre-deployment survey\n",
        "df_remote_power_quality_pre_survey = pd.read_csv(path_remote_monitoring_data_pre_survey + 'Spotlight Kampala - Sensor Installation Survey Final Dataset.csv')\n",
        "\n",
        "# Example: Accessing data from a specific sheet\n",
        "#df_gen_survey_sheet1 = gen_survey_sheets['Sheet1']  # Replace 'Sheet1' with an actual sheet name"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gS6UUGKlkx1n"
      },
      "source": [
        "### Uniformize connection type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vl-PAx_tKfpv"
      },
      "outputs": [],
      "source": [
        "# 1 = Unelectrified\n",
        "# 2 = Individual metered connection\n",
        "# 3 = Individual unmetered connection\n",
        "# 4 = Individual dual connection\n",
        "# 5 = Collective metered connection\n",
        "# 6 = Collective unmetered connection\n",
        "# 7 = Collective dual connection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6sft4ynmKO9D"
      },
      "outputs": [],
      "source": [
        "# Uniformize connection type for general survey"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6iLNQJKkiLt",
        "outputId": "71fb4dcb-0c1b-40e3-dcb1-1b67b42a6e75"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "connection_type_unencrypted\n",
            "1     24\n",
            "2    122\n",
            "3     14\n",
            "4      6\n",
            "5    225\n",
            "6     86\n",
            "7     21\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Mapping for enumerators to connection options\n",
        "enumerator_mappings = {\n",
        "    'Bulenza (Enumerator 1) (599747)': {\n",
        "        'A': 2, 'B': 3, 'C': 4, 'D': 1, 'E': 3, 'F': 6, 'G': 7, 'H': 5, 'I': 6\n",
        "    },\n",
        "    'Sumaya (Enumerator 1) (599747)': {\n",
        "        'A': 2, 'B': 3, 'C': 4, 'D': 1, 'E': 3, 'F': 6, 'G': 7, 'H': 5, 'I': 6\n",
        "    },\n",
        "    'Harry (Enumerator 2) (955451)': {\n",
        "        'A': 1, 'B': 6, 'C': 5, 'D': 2, 'E': 3, 'F': 6, 'G': 3, 'H': 7, 'I': 4\n",
        "    },\n",
        "    'Fatumah (Enumerator 3) (925225)': {\n",
        "        'A': 7, 'B': 1, 'C': 3, 'D': 5, 'E': 6, 'F': 3, 'G': 4, 'H': 2, 'I': 6\n",
        "    },\n",
        "    'Molly (Enumerator 4) (683543)': {\n",
        "        'A': 6, 'B': 7, 'C': 4, 'D': 3, 'E': 1, 'F': 5, 'G': 2, 'H': 3, 'I': 6\n",
        "    },\n",
        "    'Gerald (Enumerator 5) (135231)': {\n",
        "        'A': 5, 'B': 6, 'C': 1, 'D': 6, 'E': 2, 'F': 4, 'G': 3, 'H': 7, 'I': 3\n",
        "    }\n",
        "}\n",
        "\n",
        "# Function to map connection types based on enumerator\n",
        "\n",
        "def map_connection_type(row):\n",
        "    enumerator = row['enumerator']\n",
        "    connection = row['connection_type']\n",
        "    return enumerator_mappings.get(enumerator, {}).get(connection, np.nan)\n",
        "\n",
        "# Apply mapping\n",
        "\n",
        "df_gen_survey['connection_type_unencrypted'] = df_gen_survey.apply(map_connection_type, axis=1)\n",
        "\n",
        "# Calculate total number of respondents with each connection type\n",
        "connection_count = df_gen_survey['connection_type_unencrypted'].value_counts().sort_index()\n",
        "\n",
        "# Ensure all connection types (1 to 7) are represented\n",
        "connection_count = connection_count.reindex(range(1, 8), fill_value=0)\n",
        "\n",
        "# Display connection counts\n",
        "print(connection_count)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9HxDazMJKXG1"
      },
      "outputs": [],
      "source": [
        "# Uniformize connection type for cooking survey"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ju6hE3MXKaLf",
        "outputId": "66a82c2e-f053-4c8b-fc7f-e93707b1e39b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Connection type\n",
            "2    34\n",
            "3     2\n",
            "4     3\n",
            "5    62\n",
            "6    11\n",
            "7     8\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Define the mapping dictionary\n",
        "mapping = {'A': 2, 'B': 3, 'C': 4, 'D': 5, 'E': 6, 'F': 7}\n",
        "\n",
        "# Apply the mapping to create a new column 'Connection type'\n",
        "df_cooking_survey['Spotlight Kampala Cooking Su...']['Connection type'] = df_cooking_survey['Spotlight Kampala Cooking Su...']['What is the respondents connection modality?'].map(mapping)\n",
        "print(df_cooking_survey['Spotlight Kampala Cooking Su...']['Connection type'].value_counts().sort_index())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fenr3akq3gSO"
      },
      "outputs": [],
      "source": [
        "# Uniformize connection type for the wiring inspection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dv6lpN1n42Gx",
        "outputId": "707b28f3-433a-4565-cf9e-2cd985f5e135"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ],
      "source": [
        "print(type(df_wiring_inspection['Spotlight Kampala Wiring Ins...']))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y7B-e6S24LKJ"
      },
      "outputs": [],
      "source": [
        "# Remove single quotes from all column names\n",
        "df_wiring_inspection['Spotlight Kampala Wiring Ins...'].columns = df_wiring_inspection['Spotlight Kampala Wiring Ins...'].columns.str.replace(\"'\", \"\", regex=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zvPqKI8O3i_j",
        "outputId": "5b53f4f0-0a57-47e8-a6ed-29331d397921"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Connection type\n",
            "2    20\n",
            "3     4\n",
            "4     1\n",
            "5    43\n",
            "6    32\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "df_wiring_inspection['Spotlight Kampala Wiring Ins...']['Connection type'] = df_wiring_inspection['Spotlight Kampala Wiring Ins...']['3.11 What is the respondents connection modality?'].map(mapping)\n",
        "print(df_wiring_inspection['Spotlight Kampala Wiring Ins...']['Connection type'].value_counts().sort_index())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w2_ecZXZ74bR"
      },
      "source": [
        "### Assign exchange rate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "38BymvSglIGa"
      },
      "outputs": [],
      "source": [
        "# Assign exchange rate\n",
        "exchange_rate_gen = 3754.95 # Average exchange rate in November 2022 when the survey was completed https://www.investing.com/currencies/usd-ugx-historical-data\n",
        "exchange_rate_ecooking = 3721.72 # Average exchange rate from February to May 2023 when the e-cooking methodologies were conducted https://www.investing.com/currencies/usd-ugx-historical-data\n",
        "exchange_rate_current = 3719"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7f9V12b1PDFw"
      },
      "source": [
        "### Calculate income quartiles"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nJIlZaByPVdK"
      },
      "outputs": [],
      "source": [
        "cooking_survey_temp = df_cooking_survey['Spotlight Kampala Cooking Su...']\n",
        "cooking_survey_temp_1 = df_cooking_survey['household_roster_group']\n",
        "submission_ID = pd.unique(cooking_survey_temp['_id'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4mBjH8VzPHO4"
      },
      "outputs": [],
      "source": [
        "# Total income is calculated by asking how much each person in the household/business contributes each month\n",
        "\n",
        "# The income must be imputed by summing the incomes of people in the roster\n",
        "income_totals = np.zeros((len(submission_ID)))\n",
        "\n",
        "# Identify all rows in the roster corresponding to each respondent\n",
        "for i in range(len(submission_ID)):\n",
        "  index = cooking_survey_temp_1.index[cooking_survey_temp_1['_submission__id'] == submission_ID[i]].tolist()\n",
        "\n",
        "  # Sum the income contributions of all the people in the roster\n",
        "  income_totals[i] = cooking_survey_temp_1['How much does this person contribute each month on average?'].iloc[index].sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XDy8rvWtTBij",
        "outputId": "0891fef6-fff0-4c89-b475-192bb4688f11"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Q1: 200.0, Q3: 712.5, IQR: 512.5\n",
            "Upper Bound: 1481.25\n",
            "Number of entries before: 120, after removing outliers: 112\n"
          ]
        }
      ],
      "source": [
        "# Calculate Q1, Q3, and IQR\n",
        "Q1 = np.percentile(income_totals, 25)\n",
        "Q3 = np.percentile(income_totals, 75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "# Define upper bound for outliers\n",
        "upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "# Optionally, define a lower bound (not usually necessary for income data)\n",
        "lower_bound = Q1 - 1.5 * IQR\n",
        "\n",
        "# Remove upper outliers\n",
        "filtered_income_totals = income_totals[income_totals <= upper_bound]\n",
        "\n",
        "# Display bounds and check results\n",
        "print(f\"Q1: {Q1}, Q3: {Q3}, IQR: {IQR}\")\n",
        "print(f\"Upper Bound: {upper_bound}\")\n",
        "print(f\"Number of entries before: {len(income_totals)}, after removing outliers: {len(filtered_income_totals)}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mc9f56frP_lC"
      },
      "outputs": [],
      "source": [
        "# Create new dataframe specifying the income bracket of each respondent\n",
        "\n",
        "# Indicate how many income brackets to calculate\n",
        "income_brackets = 4 # 4 = quartile, 5 = quintile\n",
        "\n",
        "# Create a new temporary column for income_totals, aligning the filtered values\n",
        "df_cooking_survey['Spotlight Kampala Cooking Su...']['Total_income'] = np.where(\n",
        "    income_totals <= upper_bound, income_totals, np.nan\n",
        ")\n",
        "\n",
        "# Calculate income brackets using pd.qcut and handle NaN values\n",
        "df_cooking_survey['Spotlight Kampala Cooking Su...']['Income_bracket'] = pd.qcut(\n",
        "    df_cooking_survey['Spotlight Kampala Cooking Su...']['Total_income'],\n",
        "    q=income_brackets,\n",
        "    labels=np.arange(1, income_brackets + 1)\n",
        ")"
      ]
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        {
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            "text/plain": [
              "  Income Range (1,000 UGX)  Percentage of respondents (%)\n",
              "0                  0 - 200                      31.250000\n",
              "1                200 - 310                      18.750000\n",
              "2                310 - 600                      25.892857\n",
              "3              600 - 1,300                      24.107143"
            ],
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              "      <th>Income Range (1,000 UGX)</th>\n",
              "      <th>Percentage of respondents (%)</th>\n",
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              "      <td>0 - 200</td>\n",
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              "      <td>25.892857</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "quartile_summary",
              "summary": "{\n  \"name\": \"quartile_summary\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Income Range (1,000 UGX)\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"200 - 310\",\n          \"600 - 1,300\",\n          \"0 - 200\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Percentage of respondents (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.154913117764516,\n        \"min\": 18.75,\n        \"max\": 31.25,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          18.75,\n          24.107142857142858,\n          31.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calculate the ranges of income quartiles\n",
        "income_ranges = pd.qcut(filtered_income_totals, q=income_brackets, retbins=True, labels=np.arange(1, income_brackets + 1))\n",
        "\n",
        "# Extract the bin edges (ranges)\n",
        "bin_edges = income_ranges[1]\n",
        "\n",
        "# Count the number of respondents in each quartile\n",
        "quartile_counts = pd.qcut(filtered_income_totals, q=income_brackets, labels=np.arange(1, income_brackets + 1)).value_counts()\n",
        "\n",
        "# Calculate the percentage of respondents in each quartile\n",
        "quartile_percentages = (quartile_counts / len(filtered_income_totals)) * 100\n",
        "\n",
        "# Create a summary table\n",
        "quartile_summary = pd.DataFrame({\n",
        "    \"Income Range (1,000 UGX)\": [f\"{int(bin_edges[i]):,} - {int(bin_edges[i + 1]):,}\" for i in range(len(bin_edges) - 1)],\n",
        "    \"Percentage of respondents (%)\": quartile_percentages.sort_index().values\n",
        "})\n",
        "\n",
        "# Display the table\n",
        "display(quartile_summary)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jIp1j6hHW8Cp"
      },
      "source": [
        "### Uniformize expense units\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lDoRPDMbW8SS"
      },
      "outputs": [],
      "source": [
        "# Define conversion factors\n",
        "unit_conversion = {\n",
        "    'Per day': 30.44, # https://www.britannica.com/science/time/Lengths-of-years-and-months\n",
        "    'Per school term': 3/12, # Assume three school terms per year, divide by 12 for monthly\n",
        "    'Per year': 1/12, # Divide by 12 to convert annual to monthly\n",
        "    'Per week': 4}  # Multiply by 4 to convert weekly to monthly"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yx_y7nWpdObp"
      },
      "outputs": [],
      "source": [
        "conversion_expense_labels = ['Airtime and data',\n",
        "                             'School fees',\n",
        "                             'Transportation',\n",
        "                             'Food',\n",
        "                             'Water',\n",
        "                             'Sanitation',\n",
        "                             'Rent',\n",
        "                             'Loan repayment',\n",
        "                             'Entertainment',\n",
        "                             'Healthcare',\n",
        "                             'Remittances',\n",
        "                             'Business expenses']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OomepeTzdY5F"
      },
      "outputs": [],
      "source": [
        "# Define expense labels and unit labels\n",
        "expense_labels = [\n",
        "    'How much do you spend on airtime and data?',\n",
        "    'How much do you spend on school fees?',\n",
        "    'How much do you spend on transportation?',\n",
        "    'How much do you spend on food?',\n",
        "    'How much do you spend on water?',\n",
        "    'How much do you spend on sanitation?',\n",
        "    'How much do you spend on rent?',\n",
        "    'How much do you spend on loan repayment?',\n",
        "    'How much do you spend on entertainment?',\n",
        "    'How much do you spend on healthcare expenses?',\n",
        "    'How much do you spend on remittances?',\n",
        "    'How much do you spend on business expenses?'\n",
        "]\n",
        "\n",
        "expense_unit_labels = [\n",
        "    'Select the unit for airtime and data expenses.',\n",
        "    'Select the unit for school fees expenses.',\n",
        "    'Select the unit for transportation.',\n",
        "    'Select the unit for food.',\n",
        "    'Select the unit for water.',\n",
        "    'Select the unit for sanitation.',\n",
        "    'Select the unit for rent.',\n",
        "    'Select the unit for loan repayment.',\n",
        "    'Select the unit for entertainment.',\n",
        "    'Select the unit for healthcare expenses.',\n",
        "    'Select the unit for remittances.',\n",
        "    'Select the unit for business expenses.'\n",
        "]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h1FFyhdEe5Pk"
      },
      "outputs": [],
      "source": [
        "# Define the conversion function\n",
        "def convert_expenses(expense_labels, labels, expense_unit_labels, unit_conversion, df):\n",
        "    \"\"\"\n",
        "    Converts expense values to a monthly basis using the corresponding unit labels.\n",
        "\n",
        "    Parameters:\n",
        "        expense_labels (list): List of column names containing expense amounts.\n",
        "        labels (list): List of column names for the converted data.\n",
        "        expense_unit_labels (list): List of column names containing the unit of time for expenses.\n",
        "        unit_conversion (dict): Dictionary mapping units to conversion factors.\n",
        "        df (pd.DataFrame): DataFrame containing the expense and unit columns.\n",
        "\n",
        "    Returns:\n",
        "        pd.DataFrame: DataFrame with converted expenses added as new columns.\n",
        "    \"\"\"\n",
        "    for i in range(len(expense_labels)):\n",
        "        expense_col = expense_labels[i]\n",
        "        unit_col = expense_unit_labels[i]\n",
        "        converted_col = labels[i]\n",
        "\n",
        "        # Perform conversion\n",
        "        df[converted_col] = df[expense_col] * df[unit_col].map(unit_conversion).fillna(1)\n",
        "\n",
        "    return df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JgHjnjTycGsP"
      },
      "outputs": [],
      "source": [
        "# Apply the conversion function to your DataFrame\n",
        "expense_df = convert_expenses(\n",
        "    expense_labels,\n",
        "    conversion_expense_labels,\n",
        "    expense_unit_labels,\n",
        "    unit_conversion,\n",
        "    df_cooking_survey['Spotlight Kampala Cooking Su...']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oMzKDMf1pu4B"
      },
      "outputs": [],
      "source": [
        "# Define the fuel expense and unit labels\n",
        "fuel_expense_unit_labels = ['Do you buy charcoal on a daily, weekly, or monthly basis?',\n",
        "                            'Do you buy firewood on a daily, weekly, or monthly basis?',\n",
        "                            'Do you buy briquettes on a daily, weekly, or monthly basis?',\n",
        "                            'Do you buy gas on a daily, weekly, or monthly basis?']\n",
        "\n",
        "fuel_expense_labels = ['How much do you spend on charcoal each ${charcoal_expense_unit}?',\n",
        "                       'How much do you spend on firewood each ${firewood_expense_unit}?',\n",
        "                       'How much do you spend on briquettes each ${briquettes_expense_unit}?',\n",
        "                       'How much do you spend on gas each ${gas_expense_unit}?']\n",
        "\n",
        "# Define the correct labels for the fuel expense columns\n",
        "conversion_fuel_labels = ['Average monthly charcoal expense',\n",
        "                          'Average monthly firewood expense',\n",
        "                          'Average monthly briquettes expense',\n",
        "                          'Average monthly gas expense']\n",
        "\n",
        "# Conversion factors for time units\n",
        "unit_conversion_fuel = {\n",
        "    'Daily': 30.44,  # Days in a month\n",
        "    'Weekly': 4,     # Weeks in a month\n",
        "    'Monthly': 1     # Already monthly\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uoaxT2CdpgrT"
      },
      "outputs": [],
      "source": [
        "# Perform conversions for fuel expenses and add a combined column for \"Cooking fuels\"\n",
        "def convert_fuel_expenses_with_total(fuel_expense_labels, fuel_expense_unit_labels, conversion_fuel_labels, unit_conversion_fuel, df):\n",
        "    \"\"\"\n",
        "    Converts fuel expenses to a monthly basis using the corresponding unit labels and creates a combined column.\n",
        "\n",
        "    Parameters:\n",
        "        fuel_expense_labels (list): List of column names containing fuel expense amounts.\n",
        "        fuel_expense_unit_labels (list): List of column names containing the unit of time for fuel expenses.\n",
        "        conversion_fuel_labels (list): List of column names for the new converted fuel expense columns.\n",
        "        unit_conversion_fuel (dict): Dictionary mapping units to conversion factors.\n",
        "        df (pd.DataFrame): DataFrame containing the fuel expense and unit columns.\n",
        "\n",
        "    Returns:\n",
        "        pd.DataFrame: Updated DataFrame with new columns for monthly fuel expenses and a combined column.\n",
        "    \"\"\"\n",
        "    for i in range(len(fuel_expense_labels)):\n",
        "        expense_col = fuel_expense_labels[i]\n",
        "        unit_col = fuel_expense_unit_labels[i]\n",
        "        converted_col = conversion_fuel_labels[i]\n",
        "\n",
        "        # Perform conversion\n",
        "        df[converted_col] = df[expense_col] * df[unit_col].map(unit_conversion_fuel).fillna(1)\n",
        "\n",
        "    # Create the combined column \"Cooking fuels\"\n",
        "    df[\"Cooking fuels\"] = df[conversion_fuel_labels].sum(axis=1)\n",
        "\n",
        "    return df\n",
        "\n",
        "# Apply the conversion function to your DataFrame\n",
        "expense_df = convert_fuel_expenses_with_total(\n",
        "    fuel_expense_labels,\n",
        "    fuel_expense_unit_labels,\n",
        "    conversion_fuel_labels,\n",
        "    unit_conversion_fuel,\n",
        "    df_cooking_survey['Spotlight Kampala Cooking Su...']\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WJmYozeir4wr"
      },
      "outputs": [],
      "source": [
        "# Define the relevant electricity expense columns\n",
        "electricity_payment_column = 'How much do you pay on average each month to ${electricity_payment_to_list}?'\n",
        "electricity_frequency_column = 'How many times a month do you make a payment to ${electricity_payment_to_list}?'\n",
        "\n",
        "# Replace 999 with NaN in both columns of the electricity payments DataFrame\n",
        "df_cooking_survey['electricity_payments_repeat'][electricity_payment_column] = (\n",
        "    df_cooking_survey['electricity_payments_repeat'][electricity_payment_column].replace(999, np.nan)\n",
        ")\n",
        "df_cooking_survey['electricity_payments_repeat'][electricity_frequency_column] = (\n",
        "    df_cooking_survey['electricity_payments_repeat'][electricity_frequency_column].replace(999, np.nan)\n",
        ")\n",
        "\n",
        "# Multiply the payment amount by the frequency, handling NaN values appropriately\n",
        "df_cooking_survey['electricity_payments_repeat']['Electricity'] = (\n",
        "    df_cooking_survey['electricity_payments_repeat'][electricity_payment_column] *\n",
        "    df_cooking_survey['electricity_payments_repeat'][electricity_frequency_column]\n",
        ")\n",
        "\n",
        "# Merge the electricity payments data into the main expense DataFrame\n",
        "expense_df = expense_df.merge(\n",
        "    df_cooking_survey['electricity_payments_repeat'][['_submission__id', 'Electricity']],\n",
        "    left_on='_id',\n",
        "    right_on='_submission__id',\n",
        "    how='left'\n",
        ")\n",
        "\n",
        "expense_df_clean = expense_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "6Ch_KO1lqTWp",
        "outputId": "e0341de2-7f43-4d85-bce9-0b9850206f04"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      50.00\n",
              "1      60.88\n",
              "2      91.32\n",
              "3      60.88\n",
              "4      60.88\n",
              "       ...  \n",
              "118    12.00\n",
              "119    70.00\n",
              "120     8.00\n",
              "121    60.00\n",
              "122    60.00\n",
              "Name: Cooking fuels, Length: 123, dtype: float64"
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Cooking fuels</th>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>60.88</td>\n",
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              "      <th>4</th>\n",
              "      <td>60.88</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>118</th>\n",
              "      <td>12.00</td>\n",
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              "      <th>122</th>\n",
              "      <td>60.00</td>\n",
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              "</table>\n",
              "<p>123 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
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          "execution_count": 472
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        "expense_df['Cooking fuels']"
      ]
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            "text/plain": [
              "                   count        mean         std        min     25%     50%  \\\n",
              "Airtime and data   114.0   38.567193   39.884306   3.000000   20.00   30.00   \n",
              "School fees         75.0  169.721111  280.546734   0.000000   50.00  100.00   \n",
              "Transportation      59.0  167.530508  239.046133  20.000000   60.00   80.00   \n",
              "Food               119.0  338.295462  216.817739  50.000000  200.00  304.40   \n",
              "Water              103.0   29.350913   19.560973   2.000000   20.00   30.44   \n",
              "Sanitation           9.0    5.826667    9.702082   0.000000    1.00    3.00   \n",
              "Rent                78.0  141.666667   64.211838  60.000000  100.00  120.00   \n",
              "Loan repayment       6.0  170.733333  136.205825  40.000000   50.00  165.00   \n",
              "Entertainment       39.0   55.169231  132.102309  10.000000   14.50   20.00   \n",
              "Healthcare          81.0   76.561728   92.893332   2.500000   40.00   50.00   \n",
              "Remittances         41.0   86.605691   61.942062   4.166667   50.00   80.00   \n",
              "Business expenses    7.0  181.428571  125.489518  70.000000  100.00  100.00   \n",
              "Cooking fuels      123.0   63.160976   38.703785   8.000000   30.44   60.88   \n",
              "Electricity        120.0   29.683333   26.921917   5.000000   10.00   20.00   \n",
              "\n",
              "                      75%      max  NaN Count  \n",
              "Airtime and data    45.00   300.00          9  \n",
              "School fees        175.00  2000.00         48  \n",
              "Transportation     152.20  1522.00         64  \n",
              "Food               450.00  1522.00          4  \n",
              "Water               30.44   121.76         20  \n",
              "Sanitation           3.00    30.44        114  \n",
              "Rent               150.00   350.00         45  \n",
              "Loan repayment     295.00   304.40        117  \n",
              "Entertainment       25.00   608.80         84  \n",
              "Healthcare         100.00   800.00         42  \n",
              "Remittances        100.00   350.00         82  \n",
              "Business expenses  250.00   400.00        116  \n",
              "Cooking fuels       80.44   243.52          0  \n",
              "Electricity         40.00   150.00          3  "
            ],
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              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Airtime and data</th>\n",
              "      <td>114.0</td>\n",
              "      <td>38.567193</td>\n",
              "      <td>39.884306</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>20.00</td>\n",
              "      <td>30.00</td>\n",
              "      <td>45.00</td>\n",
              "      <td>300.00</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>School fees</th>\n",
              "      <td>75.0</td>\n",
              "      <td>169.721111</td>\n",
              "      <td>280.546734</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>175.00</td>\n",
              "      <td>2000.00</td>\n",
              "      <td>48</td>\n",
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              "    <tr>\n",
              "      <th>Transportation</th>\n",
              "      <td>59.0</td>\n",
              "      <td>167.530508</td>\n",
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              "      <td>20.000000</td>\n",
              "      <td>60.00</td>\n",
              "      <td>80.00</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1522.00</td>\n",
              "      <td>64</td>\n",
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              "      <th>Food</th>\n",
              "      <td>119.0</td>\n",
              "      <td>338.295462</td>\n",
              "      <td>216.817739</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>200.00</td>\n",
              "      <td>304.40</td>\n",
              "      <td>450.00</td>\n",
              "      <td>1522.00</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Water</th>\n",
              "      <td>103.0</td>\n",
              "      <td>29.350913</td>\n",
              "      <td>19.560973</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.00</td>\n",
              "      <td>30.44</td>\n",
              "      <td>30.44</td>\n",
              "      <td>121.76</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sanitation</th>\n",
              "      <td>9.0</td>\n",
              "      <td>5.826667</td>\n",
              "      <td>9.702082</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>30.44</td>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rent</th>\n",
              "      <td>78.0</td>\n",
              "      <td>141.666667</td>\n",
              "      <td>64.211838</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>100.00</td>\n",
              "      <td>120.00</td>\n",
              "      <td>150.00</td>\n",
              "      <td>350.00</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loan repayment</th>\n",
              "      <td>6.0</td>\n",
              "      <td>170.733333</td>\n",
              "      <td>136.205825</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>50.00</td>\n",
              "      <td>165.00</td>\n",
              "      <td>295.00</td>\n",
              "      <td>304.40</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Entertainment</th>\n",
              "      <td>39.0</td>\n",
              "      <td>55.169231</td>\n",
              "      <td>132.102309</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>14.50</td>\n",
              "      <td>20.00</td>\n",
              "      <td>25.00</td>\n",
              "      <td>608.80</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Healthcare</th>\n",
              "      <td>81.0</td>\n",
              "      <td>76.561728</td>\n",
              "      <td>92.893332</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>40.00</td>\n",
              "      <td>50.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>800.00</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Remittances</th>\n",
              "      <td>41.0</td>\n",
              "      <td>86.605691</td>\n",
              "      <td>61.942062</td>\n",
              "      <td>4.166667</td>\n",
              "      <td>50.00</td>\n",
              "      <td>80.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>350.00</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Business expenses</th>\n",
              "      <td>7.0</td>\n",
              "      <td>181.428571</td>\n",
              "      <td>125.489518</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>100.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>250.00</td>\n",
              "      <td>400.00</td>\n",
              "      <td>116</td>\n",
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              "      <th>Cooking fuels</th>\n",
              "      <td>123.0</td>\n",
              "      <td>63.160976</td>\n",
              "      <td>38.703785</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>30.44</td>\n",
              "      <td>60.88</td>\n",
              "      <td>80.44</td>\n",
              "      <td>243.52</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>Electricity</th>\n",
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              "      <td>29.683333</td>\n",
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              "      <td>10.00</td>\n",
              "      <td>20.00</td>\n",
              "      <td>40.00</td>\n",
              "      <td>150.00</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6dc144a1-6a19-4d4d-8c55-203e13c9e932');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-d92d13d8-089c-4826-8a6f-e9433eca1903\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d92d13d8-089c-4826-8a6f-e9433eca1903')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
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              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-d92d13d8-089c-4826-8a6f-e9433eca1903 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_d971b95c-4311-4173-a210-0712c6b32823\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('summary_statistics')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_d971b95c-4311-4173-a210-0712c6b32823 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('summary_statistics');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "summary_statistics",
              "summary": "{\n  \"name\": \"summary_statistics\",\n  \"rows\": 14,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43.54611046079473,\n        \"min\": 6.0,\n        \"max\": 123.0,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          81.0,\n          7.0,\n          114.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 89.67866840997115,\n        \"min\": 5.826666666666666,\n        \"max\": 338.295462184874,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          76.56172839506173,\n          181.42857142857142,\n          38.56719298245614\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 86.90379767654049,\n        \"min\": 9.702082250733604,\n        \"max\": 280.5467335172292,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          92.89333206412228,\n          125.48951768023912,\n          39.884305882987846\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.552788809816253,\n        \"min\": 0.0,\n        \"max\": 70.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          8.0,\n          4.166666666666666,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.88233341783232,\n        \"min\": 1.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          30.44,\n          50.0,\n          100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.05057368621486,\n        \"min\": 3.0,\n        \"max\": 304.40000000000003,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          3.0,\n          30.0,\n          50.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 125.01593336692885,\n        \"min\": 3.0,\n        \"max\": 450.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          80.44,\n          100.0,\n          45.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 615.1147834276209,\n        \"min\": 30.44,\n        \"max\": 2000.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          243.52,\n          400.0,\n          300.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NaN Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43,\n        \"min\": 0,\n        \"max\": 117,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          42,\n          116,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "expense_labels_full = ['Airtime and data',\n",
        "                       'School fees',\n",
        "                       'Transportation',\n",
        "                       'Food',\n",
        "                       'Water',\n",
        "                       'Sanitation',\n",
        "                       'Rent',\n",
        "                       'Loan repayment',\n",
        "                       'Entertainment',\n",
        "                       'Healthcare',\n",
        "                       'Remittances',\n",
        "                       'Business expenses',\n",
        "                       'Cooking fuels',\n",
        "                       'Electricity']\n",
        "\n",
        "# Generate summary statistics for the converted columns\n",
        "summary_statistics = expense_df[expense_labels_full].describe().T\n",
        "summary_statistics['NaN Count'] = expense_df[expense_labels_full].isna().sum()\n",
        "display(summary_statistics)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A6SOis9ra9Jb"
      },
      "outputs": [],
      "source": [
        "expense_df[expense_labels_full]\n",
        "expense_df_duplicate = expense_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dKuE8FEUTX2M"
      },
      "source": [
        "## Universal variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kHkHZhSm5AWC"
      },
      "outputs": [],
      "source": [
        "temp_df = df_cooking_survey['Spotlight Kampala Cooking Su...']\n",
        "temp_df1 = df_cooking_survey['household_roster_group']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U8mnH-ahyu0q"
      },
      "source": [
        "# Gender themes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zpoPs30r45WB"
      },
      "outputs": [],
      "source": [
        "# Functions used to calculate stats\n",
        "\n",
        "# Remove unwanted characters from the name dataframe\n",
        "def remove(dataframe):\n",
        "    return dataframe.str.replace(r'\\$\\{|\\}', '', regex=True)\n",
        "\n",
        "\n",
        "# Helper function to get_participant_demographics\n",
        "def get_demographic_info(actual_name, name_data, demographics_data, name_index, meal_creator):\n",
        "    if pd.notna(actual_name):\n",
        "        # Find the row in demographics_data with the matching name and adjusted index\n",
        "        demographic_info = demographics_data.loc[(demographics_data['First initial'] == actual_name) &\n",
        "                                                 (demographics_data['_parent_index'] == name_index + 1)] # Adjust index parent_index since pandas default index is 0\n",
        "        if not demographic_info.empty:\n",
        "            demographic_info = demographic_info.iloc[0]\n",
        "            return {\n",
        "                meal_creator: actual_name,\n",
        "                'Age': demographic_info['Age'],\n",
        "                'Gender': demographic_info['Gender']\n",
        "            }\n",
        "    return None\n",
        "\n",
        "# Function takes three dataframes and returns a new dataframe that maps the names who from df to df1\n",
        "def get_participant_demographics(meal_type_df, names_df, demographics_data, column, meal_intake):\n",
        "    preparers_info = []\n",
        "\n",
        "    # Iterate through our meal_df\n",
        "    for index, row in meal_type_df.iterrows():\n",
        "        if isinstance(row[column], str):\n",
        "            name_refs = row[column].split()\n",
        "            for name_ref in name_refs:\n",
        "                if name_ref in names_df.columns:\n",
        "                    # Use the name index to get the corresponding demographic info\n",
        "                    actual_name = names_df.at[index, name_ref]\n",
        "                    demographic_info = get_demographic_info(actual_name, names_df, demographics_data, index, meal_intake)\n",
        "                    if demographic_info:\n",
        "                        preparers_info.append(demographic_info)\n",
        "\n",
        "    return pd.DataFrame(preparers_info)\n",
        "\n",
        "# Helper function to create a dataframe that creates a summary for preparer and consumer\n",
        "def participant_avg_age(prep_df, consumer_df):\n",
        "    meal_type_dict = {\n",
        "        'Average Age Female Preparer': average_age(prep_df, 'Gender', 'Female'),\n",
        "        'Average Age Male Preparer': average_age(prep_df, 'Gender', 'Male'),\n",
        "        'Average Age Female Consumer': average_age(consumer_df, 'Gender', 'Female'),\n",
        "        'Average Age Male Consumer': average_age(consumer_df, 'Gender', 'Male')\n",
        "    }\n",
        "    meal_type_avg_age = pd.DataFrame(meal_type_dict, index=[0])\n",
        "    return meal_type_avg_age\n",
        "\n",
        "# Helper function to calculate average age\n",
        "def average_age(meal_type, column, gender):\n",
        "    # Filter out male and females\n",
        "    gender_filt = meal_type[column] == gender\n",
        "    average_gender_age = meal_type[gender_filt]\n",
        "    return average_gender_age['Age'].mean().round(2)\n",
        "\n",
        "# Function finds the total unique individuals who are both a preparer and consumer and returns a new dataframe showing role, count, and perctenage\n",
        "def find_common_roles(preparer_df, consumer_df, role_column_prep, role_column_cons, gender_column):\n",
        "    # Find the common individuals between preparer and consumer dataframes\n",
        "    common_individuals = preparer_df[preparer_df[role_column_prep].isin(consumer_df[role_column_cons])]\n",
        "\n",
        "    # Calculate the total number of unique individuals in both preparer and consumer dataframes\n",
        "    unique_prep = preparer_df[role_column_prep].unique()\n",
        "    unique_cons = consumer_df[role_column_cons].unique()\n",
        "    total_unique_individuals = len(set(unique_prep).union(set(unique_cons)))\n",
        "\n",
        "    # Filter the common individuals by gender\n",
        "    female_common = common_individuals[common_individuals[gender_column] == 'Female']\n",
        "    male_common = common_individuals[common_individuals[gender_column] == 'Male']\n",
        "\n",
        "    # Calculate percentages relative to the total unique individuals\n",
        "    female_percentage = (len(female_common) / total_unique_individuals) * 100 if total_unique_individuals > 0 else 0\n",
        "    male_percentage = (len(male_common) / total_unique_individuals) * 100 if total_unique_individuals > 0 else 0\n",
        "\n",
        "    # Combine the counts and percentages into a summary DataFrame\n",
        "    summary_df = pd.DataFrame({\n",
        "        'Role': ['Female Preparer and Consumer', 'Male Preparer and Consumer'],\n",
        "        'Count': [len(female_common), len(male_common)],\n",
        "        'Percentage of Total Unique Individuals': [female_percentage, male_percentage]\n",
        "    })\n",
        "\n",
        "    return summary_df.round(2)\n",
        "\n",
        "def summary_stats(dataframe, question_asked, count):\n",
        "    # Get unique value counts\n",
        "    category_column = dataframe.value_counts()\n",
        "\n",
        "    # Create a DataFrame from the value counts\n",
        "    category_table = pd.DataFrame(category_column.items(), columns=[question_asked, count])\n",
        "\n",
        "    # Add % column\n",
        "    category_table['Percentage (%)'] = category_table['Count'] / category_column.sum() * 100\n",
        "\n",
        "    print(\" \") # Visual pruposes\n",
        "    return category_table.round(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tfAVwlC64-u4"
      },
      "outputs": [],
      "source": [
        "# Get the names that were listed in the main survey dataframe and submission id\n",
        "name_df = temp_df[['name_1', 'name_2', 'name_3', 'name_4', 'name_5', 'name_6', 'name_7', 'name_8', 'name_9', 'name_10', 'name_11', 'name_12', 'name_13', 'name_14', 'name_15', '_id']]\n",
        "\n",
        "# Merge the two data frames based on submission id\n",
        "merged_df_df1 = pd.merge(temp_df[['_id']], temp_df1, left_on='_id', right_on='_submission__id', how='inner')\n",
        "\n",
        "# Extract the columns needed to find out their demographic stats\n",
        "demographics_df = merged_df_df1[['First initial', 'Age', 'Gender', 'Relationship to the head of household', '_parent_index', '_submission__id']].copy()\n",
        "demographics_df = demographics_df.rename(columns={'_submission__id': '_id'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "aFaOKE8_5gyQ",
        "outputId": "4410d8a4-1670-4f1b-e057-2ab1b8c4ed11"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  Gender Preparers  Count  Percentage (%)\n",
              "0           Female    124           83.78\n",
              "1             Male     24           16.22"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d1fef4c9-57e8-40c3-b63d-c8b9d1f1a8ad\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender Preparers</th>\n",
              "      <th>Count</th>\n",
              "      <th>Percentage (%)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>124</td>\n",
              "      <td>83.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>24</td>\n",
              "      <td>16.22</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1fef4c9-57e8-40c3-b63d-c8b9d1f1a8ad')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d1fef4c9-57e8-40c3-b63d-c8b9d1f1a8ad button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d1fef4c9-57e8-40c3-b63d-c8b9d1f1a8ad');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-0fc10e3f-462d-47f6-b226-af068f92e8b3\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0fc10e3f-462d-47f6-b226-af068f92e8b3')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-0fc10e3f-462d-47f6-b226-af068f92e8b3 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(find_common_roles(breakfast_preparer_df, breakfast_consumer_df, 'Preparer', 'Consumer', 'Gender'))\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Gender Preparers\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 70,\n        \"min\": 24,\n        \"max\": 124,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          24,\n          124\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Percentage (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47.77213413696315,\n        \"min\": 16.22,\n        \"max\": 83.78,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          16.22,\n          83.78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  Gender Consumers  Count  Percentage (%)\n",
              "0           Female    203           57.18\n",
              "1             Male    152           42.82"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cd8a501f-c2f9-4eb6-8da6-28dd850ad1d1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender Consumers</th>\n",
              "      <th>Count</th>\n",
              "      <th>Percentage (%)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>203</td>\n",
              "      <td>57.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>152</td>\n",
              "      <td>42.82</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd8a501f-c2f9-4eb6-8da6-28dd850ad1d1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cd8a501f-c2f9-4eb6-8da6-28dd850ad1d1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cd8a501f-c2f9-4eb6-8da6-28dd850ad1d1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-ba5a85a8-48ea-42fe-8e22-13c19a6d5b89\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ba5a85a8-48ea-42fe-8e22-13c19a6d5b89')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
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              "   Average Age Female Preparer  Average Age Male Preparer  \\\n",
              "0                        30.42                      24.54   \n",
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              "                           Role  Count  Percentage of Total Unique Individuals\n",
              "0  Female Preparer and Consumer    119                                   39.14\n",
              "1    Male Preparer and Consumer     24                                    7.89"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(find_common_roles(breakfast_preparer_df, breakfast_consumer_df, 'Preparer', 'Consumer', 'Gender'))\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Role\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male Preparer and Consumer\",\n          \"Female Preparer and Consumer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 67,\n        \"min\": 24,\n        \"max\": 119,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          24,\n          119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Percentage of Total Unique Individuals\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.09708691207961,\n        \"min\": 7.89,\n        \"max\": 39.14,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7.89,\n          39.14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {}
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      ],
      "source": [
        "# Filter breakfast preparers/consumers from main dataframe\n",
        "filt_by_breakfast = temp_df[['Who from the household helps prepare breakfast?', 'Who from the household normally takes breakfast at home?', '_id']].copy()\n",
        "filt_by_breakfast['Who from the household helps prepare breakfast?'] = remove(filt_by_breakfast['Who from the household helps prepare breakfast?'])\n",
        "filt_by_breakfast['Who from the household normally takes breakfast at home?'] = remove(filt_by_breakfast['Who from the household normally takes breakfast at home?'])\n",
        "\n",
        "# Create a dataframe for each breakfast preparer/consumer\n",
        "breakfast_preparer_df = get_participant_demographics(filt_by_breakfast, name_df, demographics_df, 'Who from the household helps prepare breakfast?', 'Preparer')\n",
        "breakfast_consumer_df = get_participant_demographics(filt_by_breakfast, name_df, demographics_df, 'Who from the household normally takes breakfast at home?', 'Consumer')\n",
        "\n",
        "# Gender composition\n",
        "display(summary_stats(breakfast_preparer_df['Gender'], 'Gender Preparers', 'Count'))\n",
        "display(summary_stats(breakfast_consumer_df['Gender'], 'Gender Consumers', 'Count'))\n",
        "\n",
        "# Average age for prep/consumer\n",
        "display(participant_avg_age(breakfast_preparer_df, breakfast_consumer_df))\n",
        "\n",
        "# Coincidence of uniquie individuals who are both a meal preparer and consumer\n",
        "display(find_common_roles(breakfast_preparer_df, breakfast_consumer_df, 'Preparer', 'Consumer', 'Gender'))"
      ]
    },
    {
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            "text/plain": [
              "  Gender Preparers  Count  Percentage (%)\n",
              "0           Female    110            87.3\n",
              "1             Male     16            12.7"
            ],
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              "\n",
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              "        (() => {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(find_common_roles(lunch_preparer_df, lunch_consumer_df, 'Preparer', 'Consumer', 'Gender'))\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Gender Preparers\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 66,\n        \"min\": 16,\n        \"max\": 110,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          16,\n          110\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Percentage (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.75016587651644,\n        \"min\": 12.7,\n        \"max\": 87.3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          12.7,\n          87.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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              "  Gender Consumers  Count  Percentage (%)\n",
              "0           Female    176           60.07\n",
              "1             Male    117           39.93"
            ],
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              "   Average Age Female Preparer  Average Age Male Preparer  \\\n",
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              "                           Role  Count  Percentage of Total Unique Individuals\n",
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              "summary": "{\n  \"name\": \"display(find_common_roles(lunch_preparer_df, lunch_consumer_df, 'Preparer', 'Consumer', 'Gender'))\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Role\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male Preparer and Consumer\",\n          \"Female Preparer and Consumer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62,\n        \"min\": 16,\n        \"max\": 104,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          16,\n          104\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Percentage of Total Unique Individuals\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.129462812611965,\n        \"min\": 5.95,\n        \"max\": 38.66,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.95,\n          38.66\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Filter lunch preparers/consumers from main dataframe\n",
        "filt_by_lunch = temp_df[['Who from the household helps prepare lunch?', 'Who from the household normally takes lunch at home?', '_id']].copy()\n",
        "filt_by_lunch['Who from the household helps prepare lunch?'] = remove(filt_by_lunch['Who from the household helps prepare lunch?'])\n",
        "filt_by_lunch['Who from the household normally takes lunch at home?'] = remove(filt_by_lunch['Who from the household normally takes lunch at home?'])\n",
        "\n",
        "# Create a dataframe for each lunch preparer/consumer\n",
        "lunch_preparer_df = get_participant_demographics(filt_by_lunch, name_df, demographics_df, 'Who from the household helps prepare lunch?', 'Preparer')\n",
        "lunch_consumer_df = get_participant_demographics(filt_by_lunch, name_df, demographics_df, 'Who from the household normally takes lunch at home?', 'Consumer')\n",
        "\n",
        "# Gender composition\n",
        "display(summary_stats(lunch_preparer_df['Gender'], 'Gender Preparers', 'Count'))\n",
        "display(summary_stats(lunch_consumer_df['Gender'], 'Gender Consumers', 'Count'))\n",
        "\n",
        "# Average age\n",
        "display(participant_avg_age(lunch_preparer_df, lunch_consumer_df))\n",
        "\n",
        "# Coincidence of uniquie individuals who are both a meal preparer and consumer\n",
        "display(find_common_roles(lunch_preparer_df, lunch_consumer_df, 'Preparer', 'Consumer', 'Gender'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "base_uri": "https://localhost:8080/",
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        "id": "jme-wp_r6U02",
        "outputId": "1a72a193-0108-4203-ef88-531c018dfcc8"
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          "name": "stdout",
          "text": [
            " \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  Gender Preparers  Count  Percentage (%)\n",
              "0           Female    107            85.6\n",
              "1             Male     18            14.4"
            ],
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              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(find_common_roles(dinner_preparer_df, dinner_consumer_df, 'Preparer', 'Consumer', 'Gender'))\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Gender Preparers\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62,\n        \"min\": 18,\n        \"max\": 107,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          18,\n          107\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Percentage (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50.346002820482184,\n        \"min\": 14.4,\n        \"max\": 85.6,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          14.4,\n          85.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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              "  Gender Consumers  Count  Percentage (%)\n",
              "0           Female    167           56.61\n",
              "1             Male    128           43.39"
            ],
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              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
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              "\n",
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              "   Average Age Female Preparer  Average Age Male Preparer  \\\n",
              "0                        30.42                      28.22   \n",
              "\n",
              "   Average Age Female Consumer  Average Age Male Consumer  \n",
              "0                        25.41                      24.06  "
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "                           Role  Count  Percentage of Total Unique Individuals\n",
              "0  Female Preparer and Consumer    107                                   41.31\n",
              "1    Male Preparer and Consumer     18                                    6.95"
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(find_common_roles(dinner_preparer_df, dinner_consumer_df, 'Preparer', 'Consumer', 'Gender'))\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Role\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male Preparer and Consumer\",\n          \"Female Preparer and Consumer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62,\n        \"min\": 18,\n        \"max\": 107,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          18,\n          107\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Percentage of Total Unique Individuals\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.296189001569775,\n        \"min\": 6.95,\n        \"max\": 41.31,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          6.95,\n          41.31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Filter dinner preparers/consumers from main dataframe\n",
        "filt_by_dinner = temp_df[['Who from the household helps prepare dinner?', 'Who from the household normally takes dinner at home?', '_id']].copy()\n",
        "filt_by_dinner['Who from the household helps prepare dinner?'] = remove(filt_by_dinner['Who from the household helps prepare dinner?'])\n",
        "filt_by_dinner['Who from the household normally takes dinner at home?'] = remove(filt_by_dinner['Who from the household normally takes dinner at home?'])\n",
        "\n",
        "# Create a dataframe for each dinner preparer/consumer\n",
        "dinner_preparer_df = get_participant_demographics(filt_by_dinner, name_df, demographics_df, 'Who from the household helps prepare dinner?', 'Preparer')\n",
        "dinner_consumer_df = get_participant_demographics(filt_by_dinner, name_df, demographics_df, 'Who from the household normally takes dinner at home?', 'Consumer')\n",
        "\n",
        "# Gender composition\n",
        "display(summary_stats(dinner_preparer_df['Gender'], 'Gender Preparers', 'Count'))\n",
        "display(summary_stats(dinner_consumer_df['Gender'], 'Gender Consumers', 'Count'))\n",
        "\n",
        "# Average age\n",
        "display(participant_avg_age(dinner_preparer_df, dinner_consumer_df))\n",
        "\n",
        "# Coincidence of uniquie individuals who are both a meal preparer and consumer\n",
        "display(find_common_roles(dinner_preparer_df, dinner_consumer_df, 'Preparer', 'Consumer', 'Gender'))"
      ]
    },
    {
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      "execution_count": null,
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        "id": "zFlYQyY56X7-",
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          "text": [
            " \n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "  Gender Preparers  Count  Percentage (%)\n",
              "0           Female     27            87.1\n",
              "1             Male      4            12.9"
            ],
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              "  Gender Consumers  Count  Percentage (%)\n",
              "0           Female     39           63.93\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "   Average Age Female Preparer  Average Age Male Preparer  \\\n",
              "0                        24.81                       23.0   \n",
              "\n",
              "   Average Age Female Consumer  Average Age Male Consumer  \n",
              "0                        22.41                      28.68  "
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "                           Role  Count  Percentage of Total Unique Individuals\n",
              "0  Female Preparer and Consumer     27                                   45.76\n",
              "1    Male Preparer and Consumer      4                                    6.78"
            ],
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-046c0816-bc10-4359-b4d2-8cf8063785fd button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(find_common_roles(snack_preparer_df, snack_consumer_df, 'Preparer', 'Consumer', 'Gender'))\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Role\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male Preparer and Consumer\",\n          \"Female Preparer and Consumer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 4,\n        \"max\": 27,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Percentage of Total Unique Individuals\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.56302233065162,\n        \"min\": 6.78,\n        \"max\": 45.76,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          6.78,\n          45.76\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Filter snack preparers/consumers from main dataframe\n",
        "filt_by_snack = temp_df[['Who from the household helps prepare snacks?', 'Who from the household normally takes snacks at home?', '_id']].copy()\n",
        "filt_by_snack['Who from the household helps prepare snacks?'] = remove(filt_by_snack['Who from the household helps prepare snacks?'])\n",
        "filt_by_snack['Who from the household normally takes snacks at home?'] = remove(filt_by_snack['Who from the household normally takes snacks at home?'])\n",
        "\n",
        "# Create a dataframe for each dinner preparer/consumer\n",
        "snack_preparer_df = get_participant_demographics(filt_by_snack, name_df, demographics_df, 'Who from the household helps prepare snacks?', 'Preparer')\n",
        "snack_consumer_df = get_participant_demographics(filt_by_snack, name_df, demographics_df, 'Who from the household normally takes snacks at home?', 'Consumer')\n",
        "\n",
        "# Gender composition\n",
        "display(summary_stats(snack_preparer_df['Gender'], 'Gender Preparers', 'Count'))\n",
        "display(summary_stats(snack_consumer_df['Gender'], 'Gender Consumers', 'Count'))\n",
        "\n",
        "# Average age\n",
        "display(participant_avg_age(snack_preparer_df, snack_consumer_df))\n",
        "\n",
        "# Coincidence of uniquie individuals who are both a meal preparer and consumer\n",
        "display(find_common_roles(snack_preparer_df, snack_consumer_df, 'Preparer', 'Consumer', 'Gender'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g8ArxQUY8T3w",
        "outputId": "87bebe52-9bbb-4d62-ef50-7640db109e52"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " \n",
            " \n",
            " \n",
            " \n",
            " \n",
            " \n",
            " \n",
            " \n"
          ]
        }
      ],
      "source": [
        "# Extract percentages for preparers and consumers for each meal\n",
        "meal_data = {\n",
        "    'Breakfast': {\n",
        "        'preparer': summary_stats(breakfast_preparer_df['Gender'], 'Gender Preparers', 'Count')['Percentage (%)'].to_dict(),\n",
        "        'consumer': summary_stats(breakfast_consumer_df['Gender'], 'Gender Consumers', 'Count')['Percentage (%)'].to_dict(),\n",
        "    },\n",
        "    'Lunch': {\n",
        "        'preparer': summary_stats(lunch_preparer_df['Gender'], 'Gender Preparers', 'Count')['Percentage (%)'].to_dict(),\n",
        "        'consumer': summary_stats(lunch_consumer_df['Gender'], 'Gender Consumers', 'Count')['Percentage (%)'].to_dict(),\n",
        "    },\n",
        "    'Dinner': {\n",
        "        'preparer': summary_stats(dinner_preparer_df['Gender'], 'Gender Preparers', 'Count')['Percentage (%)'].to_dict(),\n",
        "        'consumer': summary_stats(dinner_consumer_df['Gender'], 'Gender Consumers', 'Count')['Percentage (%)'].to_dict(),\n",
        "    },\n",
        "    'Snacks': {\n",
        "        'preparer': summary_stats(snack_preparer_df['Gender'], 'Gender Preparers', 'Count')['Percentage (%)'].to_dict(),\n",
        "        'consumer': summary_stats(snack_consumer_df['Gender'], 'Gender Consumers', 'Count')['Percentage (%)'].to_dict(),\n",
        "    }\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get all survey _id values where anyone was listed as a preparer or consumer\n",
        "all_survey_ids = pd.concat([\n",
        "    filt_by_breakfast[['_id']],\n",
        "    filt_by_lunch[['_id']],\n",
        "    filt_by_dinner[['_id']],\n",
        "    filt_by_snack[['_id']]\n",
        "], ignore_index=True).drop_duplicates()\n",
        "\n",
        "print(f\"Number of unique survey responses included: {all_survey_ids['_id'].nunique()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "03zs8Gvw6r3p",
        "outputId": "8ae01981-3ae2-4d9e-f0b3-31981f5df7ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of unique survey responses included: 120\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "trNj0qui8fuU",
        "outputId": "2470b209-9c38-4d95-84b7-40e9593fd6c7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Generate the nested pie charts\n",
        "fig, axes = plt.subplots(2, 2, figsize=(12, 12))\n",
        "\n",
        "# Define colors for the legend\n",
        "preparer_color = '#71b3ff'\n",
        "consumer_color = 'green'\n",
        "\n",
        "# Map meals to axes for plotting\n",
        "meal_axes = {\n",
        "    'Breakfast': axes[0, 0],\n",
        "    'Lunch': axes[0, 1],\n",
        "    'Dinner': axes[1, 0],\n",
        "    'Snacks': axes[1, 1],\n",
        "}\n",
        "\n",
        "# Create the nested pie charts\n",
        "for meal, ax in meal_axes.items():\n",
        "    data = meal_data[meal]\n",
        "\n",
        "    # Outer pie (Preparer data)\n",
        "    outer_sizes = list(data['preparer'].values())\n",
        "    outer_labels = list(data['preparer'].keys())\n",
        "\n",
        "    # Inner pie (Consumer data)\n",
        "    inner_sizes = list(data['consumer'].values())\n",
        "    inner_labels = list(data['consumer'].keys())\n",
        "\n",
        "    # Plot the outer pie (Preparer data)\n",
        "    wedges_outer, texts_outer = ax.pie(\n",
        "        outer_sizes, radius=1, labels=None,  # Remove all labels\n",
        "        wedgeprops={'width': 0.3, 'edgecolor': 'w'},\n",
        "        colors=[preparer_color if i == 0 else 'white' for i in range(len(outer_sizes))]\n",
        "    )\n",
        "\n",
        "    # Add black label for the first entry in the outer pie\n",
        "    if len(wedges_outer) > 0:\n",
        "        angle = (wedges_outer[0].theta2 + wedges_outer[0].theta1) / 2\n",
        "        x = 1.1 * np.cos(np.deg2rad(angle))\n",
        "        y = 1.1 * np.sin(np.deg2rad(angle))\n",
        "        ax.text(x, y, f\"{round(outer_sizes[0])}%\", ha='center', va='center', color='black')\n",
        "\n",
        "    # Plot the inner pie (Consumer data)\n",
        "    wedges_inner, texts_inner = ax.pie(\n",
        "        inner_sizes, radius=0.7, labels=None,  # Remove all labels\n",
        "        wedgeprops={'width': 0.3, 'edgecolor': 'w'},\n",
        "        colors=[consumer_color if i == 0 else 'white' for i in range(len(inner_sizes))]\n",
        "    )\n",
        "\n",
        "    # Add black label for the first entry in the inner pie\n",
        "    if len(wedges_inner) > 0:\n",
        "        angle = (wedges_inner[0].theta2 + wedges_inner[0].theta1) / 2\n",
        "        x = 0.9 * np.cos(np.deg2rad(angle))\n",
        "        y = 0.9 * np.sin(np.deg2rad(angle))\n",
        "        ax.text(x, y, f\"{round(inner_sizes[0])}%\", ha='center', va='center', color='black')\n",
        "\n",
        "    # Set the title for each pie chart\n",
        "    ax.set_title(meal, fontsize=16)\n",
        "\n",
        "# Add a legend for the colors\n",
        "fig.legend(['Meal preparers', 'Meal takers'], loc='upper center', fontsize=14,\n",
        "           ncol=2, frameon=False, markerscale=1.5,\n",
        "           prop={'size': 14}, bbox_to_anchor=(0.5, 0.95))\n",
        "\n",
        "# Adjust layout and show the plot\n",
        "plt.tight_layout(rect=[0, 0, 1, 0.93])  # Leave space for the legend\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "id": "IaQ27fIQ-gaG",
        "outputId": "fe3f1da7-0dd2-4e77-ec73-65c8ea8747f5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Initialize dictionaries for total preparers and consumers\n",
        "total_preparers = {'Female': 0, 'Male': 0}\n",
        "total_consumers = {'Female': 0, 'Male': 0}\n",
        "\n",
        "# Combine data across all meals\n",
        "for meal, data in meal_data.items():\n",
        "    for gender, value in data['preparer'].items():\n",
        "        # Ensure gender keys are correctly mapped\n",
        "        gender_label = 'Female' if gender in ['Female', 0] else 'Male'\n",
        "        total_preparers[gender_label] += value\n",
        "    for gender, value in data['consumer'].items():\n",
        "        gender_label = 'Female' if gender in ['Female', 0] else 'Male'\n",
        "        total_consumers[gender_label] += value\n",
        "\n",
        "# Normalize percentages\n",
        "total_preparer_sum = sum(total_preparers.values())\n",
        "total_consumer_sum = sum(total_consumers.values())\n",
        "preparer_percentages = {k: (v / total_preparer_sum) * 100 for k, v in total_preparers.items()}\n",
        "consumer_percentages = {k: (v / total_consumer_sum) * 100 for k, v in total_consumers.items()}\n",
        "\n",
        "# Prepare data for the single pie chart\n",
        "outer_sizes = list(preparer_percentages.values())\n",
        "outer_labels = list(preparer_percentages.keys())\n",
        "inner_sizes = list(consumer_percentages.values())\n",
        "inner_labels = list(consumer_percentages.keys())\n",
        "\n",
        "# Create the single nested pie chart\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "\n",
        "# Plot the outer pie (Meal preparers)\n",
        "ax.pie(\n",
        "    outer_sizes, radius=1, labels=None,  # Remove all labels\n",
        "    wedgeprops={'width': 0.3, 'edgecolor': 'w'},\n",
        "    colors=['#71b3ff', 'white']  # Blue for Female, White for Male\n",
        ")\n",
        "\n",
        "# Plot the inner pie (Meal takers)\n",
        "ax.pie(\n",
        "    inner_sizes, radius=0.7, labels=None,  # Remove all labels\n",
        "    wedgeprops={'width': 0.3, 'edgecolor': 'w'},\n",
        "    colors=['#e3555b', 'white']  # Green for Female, White for Male\n",
        ")\n",
        "\n",
        "# Add custom labels manually\n",
        "# Manually specify the (x, y) coordinates for your labels\n",
        "custom_labels = [\n",
        "    {'text': '86%', 'x': 0.65, 'y': -0.6},  # Adjust x, y for placement\n",
        "    {'text': '59%', 'x': -0.35, 'y': -0.35}  # Adjust x, y for placement\n",
        "]\n",
        "\n",
        "for label in custom_labels:\n",
        "    ax.text(label['x'], label['y'], label['text'], ha='center', va='center',\n",
        "            fontsize=14, color='black')\n",
        "\n",
        "# Add a legend\n",
        "handles = [plt.Line2D([0], [0], color='#71b3ff', lw=10),\n",
        "           plt.Line2D([0], [0], color='#e3555b', lw=10)]\n",
        "fig.legend(handles, ['Meal preparers', 'Meal takers'], loc='center',\n",
        "           fontsize=14, frameon=False, bbox_to_anchor=(0.8, 0.4))\n",
        "\n",
        "# Adjust layout and show the plot\n",
        "plt.tight_layout(rect=[0, 0, 1, 0.9])  # Leave space for the legend\n",
        "\n",
        "plt.savefig(fig_path + \"Gender Roles in Meal Preparation.png\", dpi=500)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Womens' roles in household"
      ],
      "metadata": {
        "id": "5HOO2eDBUIDC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the mapping of full column names to custom labels\n",
        "label_mapping = {\n",
        "#    'Is this person primarily responsible for cooking?': 'Cook',\n",
        "    'Is this person involved in managing household expenses?': 'Financial manager',\n",
        "    'Is this person responsible for making big decisions about the household, like where to live?': 'Household decision-maker',\n",
        "    'Does this person earn money for the household?': 'Financial provider'\n",
        "}\n",
        "\n",
        "# Specify the order in which to display the labels\n",
        "ordered_columns = list(label_mapping.keys())  # List of full column names in desired order\n",
        "custom_labels = [label_mapping[col] for col in ordered_columns]  # Corresponding custom labels\n",
        "\n",
        "# Filter out rows with missing gender values\n",
        "df_filtered = df_cooking_survey['household_roster_group'].dropna(subset=['Gender'])\n",
        "\n",
        "# Count the number of male and female respondents\n",
        "gender_counts = df_filtered['Gender'].value_counts()\n",
        "total_male = gender_counts.get('Male', 1)  # Avoid division by zero\n",
        "total_female = gender_counts.get('Female', 1)\n",
        "\n",
        "# Compute the normalization factor to adjust for equal gender representation\n",
        "total_responses = total_male + total_female\n",
        "male_weight = total_responses / (2 * total_male)  # Scale males up if they are underrepresented\n",
        "female_weight = total_responses / (2 * total_female)  # Scale females down if they are overrepresented\n",
        "\n",
        "# Convert 'Yes' responses into binary values (1 = Yes, 0 = No), ensuring data type is float64\n",
        "yes_counts = df_filtered[ordered_columns].apply(lambda x: x.eq('Yes')).astype(float)\n",
        "yes_counts['Gender'] = df_filtered['Gender']\n",
        "\n",
        "# Apply the gender weighting\n",
        "yes_counts.loc[yes_counts['Gender'] == 'Male', ordered_columns] *= male_weight\n",
        "yes_counts.loc[yes_counts['Gender'] == 'Female', ordered_columns] *= female_weight\n",
        "\n",
        "# Group by gender and sum the weighted 'Yes' responses\n",
        "yes_counts_grouped = yes_counts.groupby('Gender')[ordered_columns].sum()\n",
        "\n",
        "# Convert counts into percentages (stacked percentage format)\n",
        "yes_percentages = yes_counts_grouped.div(yes_counts_grouped.sum(axis=0), axis=1) * 100\n",
        "\n",
        "# Transpose for plotting\n",
        "yes_percentages = yes_percentages.T\n",
        "\n",
        "# Rename the rows using the custom labels\n",
        "yes_percentages.index = custom_labels\n",
        "\n",
        "# Define custom colors\n",
        "colors = ['#71b3ff', '#e3555b']  # Male = Blue, Female = Red\n",
        "\n",
        "# Increase font sizes\n",
        "axis_fontsize = 14\n",
        "tick_fontsize = 14\n",
        "legend_fontsize = 14\n",
        "\n",
        "# Plot the stacked horizontal bar chart with adjusted width/height\n",
        "fig, ax = plt.subplots(figsize=(10, 4))  # Shorter and wider\n",
        "\n",
        "yes_percentages.plot(kind='barh', stacked=True, ax=ax, width=0.7, color=colors)\n",
        "display(yes_percentages)\n",
        "# Formatting\n",
        "ax.set_xlabel('Percentage of respondents', fontsize=axis_fontsize)  # Removed (%)\n",
        "ax.set_ylabel('')\n",
        "ax.set_xlim(0, 100)\n",
        "ax.set_yticklabels(custom_labels, fontsize=tick_fontsize)\n",
        "ax.tick_params(axis='x', labelsize=tick_fontsize)\n",
        "\n",
        "# Format x-axis ticks to include the \"%\" symbol\n",
        "ax.set_xticks(range(0, 101, 20))  # Example tick marks at intervals of 20\n",
        "ax.set_xticklabels([f\"{x}%\" for x in range(0, 101, 20)], fontsize=tick_fontsize)  # Adding %\n",
        "\n",
        "# Move legend to top-right, outside the plot area\n",
        "ax.legend(\n",
        "    title='Gender', fontsize=legend_fontsize, title_fontsize=legend_fontsize,\n",
        "    frameon=False, loc='upper right', bbox_to_anchor=(1.4, 1)\n",
        ")\n",
        "\n",
        "# Remove spines for cleaner look\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "\n",
        "# Adjust layout and show the plot\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig(fig_path + \"Household Gender Roles.png\", dpi=500)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "3hTI0vDsUQps",
        "outputId": "8286b84f-5fbf-4b95-9871-208bac70db53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Gender                       Female       Male\n",
              "Financial manager         60.174152  39.825848\n",
              "Household decision-maker  52.898127  47.101873\n",
              "Financial provider        48.579271  51.420729"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-448b49fe-0c9c-4c0d-ab94-6e471daf1725\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Gender</th>\n",
              "      <th>Female</th>\n",
              "      <th>Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Financial manager</th>\n",
              "      <td>60.174152</td>\n",
              "      <td>39.825848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Household decision-maker</th>\n",
              "      <td>52.898127</td>\n",
              "      <td>47.101873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Financial provider</th>\n",
              "      <td>48.579271</td>\n",
              "      <td>51.420729</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-448b49fe-0c9c-4c0d-ab94-6e471daf1725')\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-448b49fe-0c9c-4c0d-ab94-6e471daf1725 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-448b49fe-0c9c-4c0d-ab94-6e471daf1725');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-d5faabd9-45d2-49b5-a5de-44aa0bc83a02 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_04580068-62c0-48a4-a172-4e86e9973f18\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('yes_percentages')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_04580068-62c0-48a4-a172-4e86e9973f18 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('yes_percentages');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "yes_percentages",
              "summary": "{\n  \"name\": \"yes_percentages\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Female\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.859953602775712,\n        \"min\": 48.579270683570776,\n        \"max\": 60.17415215398717,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          60.17415215398717,\n          52.89812714816806,\n          48.579270683570776\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Male\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.859953602775715,\n        \"min\": 39.825847846012834,\n        \"max\": 51.42072931642924,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          39.825847846012834,\n          47.10187285183194,\n          51.42072931642924\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zR0N5M445zh"
      },
      "source": [
        "## Appliance ownership rate of female business owners\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        },
        "id": "ZMYAu4dRyytP",
        "outputId": "0cb65185-436b-4178-bd8c-715e0b376505"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-444-9452fe4e8753>:25: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Filter respondents where 'gender' is 'female' and 'home_business_both' is 'business' or 'both'\n",
        "df_female_business = df_gen_survey[\n",
        "    (df_gen_survey['gender'] == 'female') &\n",
        "    (df_gen_survey['home_business_both'].isin(['business', 'both']))\n",
        "]\n",
        "\n",
        "# Mapping for grouping the values in 'appliance_for_business_other1'\n",
        "group_mapping = {\n",
        "    'Dryer': 'Other',\n",
        "    'Hair dryer': 'Hair dryer/clippers',\n",
        "    'Flat iron hand dryer': 'Hair dryer/clippers',\n",
        "    'Ice cream marchines': 'Other',\n",
        "    'Deep freezer\\nDeep flyer': 'Refrigerator',\n",
        "    'Deep freezer': 'Refrigerator',\n",
        "    'Popcorn marchine': 'Popcorn machine',\n",
        "    'Photo copy marchine': 'Popcorn machine',\n",
        "    'Popcorn machine': 'Popcorn machine',\n",
        "    'Hand dryer': 'Hair dryer/clippers',\n",
        "    'Hair cutter': 'Hair dryer/clippers',\n",
        "    'Welding machine': 'Other',\n",
        "    'Sewing machine': 'Other'\n",
        "}\n",
        "\n",
        "# Standardizing the column using the mapping\n",
        "df_female_business['appliance_for_business_other1_grouped'] = df_female_business['appliance_for_business_other1'].map(group_mapping)\n",
        "\n",
        "# Count occurrences of each group (dropping NaN)\n",
        "grouped_counts = df_female_business['appliance_for_business_other1_grouped'].dropna().value_counts()\n",
        "\n",
        "# Combine with existing appliance counts\n",
        "appliance_columns = [\n",
        "    'appliance_for_business_lighting', 'appliance_for_business_phone_charger',\n",
        "    'appliance_for_business_radio', 'appliance_for_business_tv',\n",
        "    'appliance_for_business_fan', 'appliance_for_business_refrigerator',\n",
        "    'appliance_for_business_iron', 'appliance_for_business_coils',\n",
        "    'appliance_for_business_electric_kettle', 'appliance_for_business_epc',\n",
        "    'appliance_for_business_computer', 'appliance_for_business_blender',\n",
        "    'appliance_for_business_microwave', 'appliance_for_business_printer',\n",
        "    'appliance_for_business_audio', 'appliance_for_business_other',\n",
        "    'appliance_for_business_none'\n",
        "]\n",
        "\n",
        "appliance_labels = [\n",
        "    'Lighting', 'Phone charger', 'Radio', 'TV', 'Fan', 'Refrigerator',\n",
        "    'Iron', 'Cooking coils', 'Electric kettle', 'EPC', 'Computer',\n",
        "    'Blender', 'Microwave', 'Printer', 'Audio', 'Other', 'None'\n",
        "]\n",
        "\n",
        "# Count the original appliance occurrences\n",
        "appliance_counts = df_female_business[appliance_columns].sum()\n",
        "appliance_counts.index = appliance_labels\n",
        "\n",
        "# Add the grouped counts to the chart\n",
        "final_counts = appliance_counts.add(grouped_counts, fill_value=0).sort_values()\n",
        "\n",
        "# Remove groups with 0 responses\n",
        "final_counts = final_counts[final_counts > 0]\n",
        "\n",
        "# Convert to percentages\n",
        "total_respondents = len(df_female_business)\n",
        "final_percentages = (final_counts / total_respondents) * 100\n",
        "\n",
        "final_percentages = final_percentages[final_percentages.index != 'Other']\n",
        "\n",
        "# Plot the horizontal bar chart\n",
        "plt.figure(figsize=(10, 8))\n",
        "final_percentages.plot(kind='barh', color='#444ab4')\n",
        "plt.title('Appliance ownership rates for female entrepreneurs ', fontsize=20)\n",
        "plt.xlabel('Percentage of small businesswomen', fontsize=16)\n",
        "plt.xticks(fontsize=14)\n",
        "plt.yticks(fontsize=14)\n",
        "plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{x:.0f}%'))  # Format x-axis as percentages\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Household internal decision-making around appliance purchase"
      ],
      "metadata": {
        "id": "pP_yhiL2tLrQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Extract relevant DataFrames\n",
        "df_main = df_cooking_survey['Spotlight Kampala Cooking Su...']\n",
        "df_roster = df_cooking_survey['household_roster_group']\n",
        "\n",
        "# Map gender to the main DataFrame by finding the first matching entry in the household roster\n",
        "gender_map = df_roster.groupby('_submission__id')['Gender'].first()\n",
        "df_main['Gender'] = df_main['_id'].map(gender_map)\n",
        "\n",
        "# Define the response column\n",
        "response_col = 'If you wanted to buy and use an electric appliance like an electric pressure cooker, are there people in your household who would need to agree?'\n",
        "\n",
        "# Calculate the proportion of responses disaggregated by gender\n",
        "proportion_by_gender = df_main.groupby(['Gender', response_col]).size().unstack().apply(lambda x: x / x.sum(), axis=1)\n",
        "\n",
        "# Display the results\n",
        "display(proportion_by_gender)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "BVPc-HaengIj",
        "outputId": "35d4bca2-3d8a-4830-ed67-d5a9303d583b"
      },
      "execution_count": null,
      "outputs": [
        {
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            "text/plain": [
              "If you wanted to buy and use an electric appliance like an electric pressure cooker, are there people in your household who would need to agree?        No  \\\n",
              "Gender                                                                                                                                                       \n",
              "Female                                                                                                                                            0.734043   \n",
              "Male                                                                                                                                              0.884615   \n",
              "\n",
              "If you wanted to buy and use an electric appliance like an electric pressure cooker, are there people in your household who would need to agree?       Yes  \n",
              "Gender                                                                                                                                                      \n",
              "Female                                                                                                                                            0.265957  \n",
              "Male                                                                                                                                              0.115385  "
            ],
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              "      <th>If you wanted to buy and use an electric appliance like an electric pressure cooker, are there people in your household who would need to agree?</th>\n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"proportion_by_gender\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"No\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10647107016229518,\n        \"min\": 0.7340425531914894,\n        \"max\": 0.8846153846153846,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8846153846153846,\n          0.7340425531914894\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Yes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10647107016229519,\n        \"min\": 0.11538461538461539,\n        \"max\": 0.26595744680851063,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.11538461538461539,\n          0.26595744680851063\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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    },
    {
      "cell_type": "code",
      "source": [
        "# Extract relevant DataFrames\n",
        "df_main = df_cooking_survey['Spotlight Kampala Cooking Su...']\n",
        "df_roster = df_cooking_survey['household_roster_group']\n",
        "\n",
        "# Step 1: Map gender to the main DataFrame by finding the first matching entry in the household roster\n",
        "gender_map = df_roster.groupby('_submission__id')['Gender'].first()\n",
        "df_main['Gender'] = df_main['_id'].map(gender_map)\n",
        "\n",
        "# Define relevant columns\n",
        "response_col = 'If you wanted to buy and use an electric appliance like an electric pressure cooker, are there people in your household who would need to agree?'\n",
        "agreement_col = 'Who in your household would need to agree?'\n",
        "\n",
        "# Step 2: Calculate the percentage of respondents who answered \"Yes\"\n",
        "total_responses = len(df_main)\n",
        "yes_responses = df_main[response_col].eq(\"Yes\").sum()\n",
        "yes_percentage = (yes_responses / total_responses) * 100\n",
        "\n",
        "# Print the statistic\n",
        "print(f\"Percentage of respondents who require household agreement: {yes_percentage:.1f}%\")\n",
        "\n",
        "# Step 3: Extract gender and household roles of those who need to agree\n",
        "def get_relationships_and_gender(row):\n",
        "    if pd.isna(row[agreement_col]):\n",
        "        return []\n",
        "\n",
        "    # Extract numeric indices from ${name_1} ${name_2} format\n",
        "    indices = [int(match) - 1 for match in re.findall(r'\\$\\{name_(\\d+)\\}', row[agreement_col])]\n",
        "\n",
        "    results = []\n",
        "    for idx in indices:\n",
        "        match = df_roster[(df_roster['_submission__id'] == row['_id'])].iloc[idx]  # Select nth row\n",
        "        if 'Relationship to the head of household' in match and 'Gender' in match:\n",
        "            results.append((match['Relationship to the head of household'], match['Gender']))\n",
        "\n",
        "    return results\n",
        "\n",
        "# Apply function to get relationships and gender\n",
        "df_filtered = df_main[df_main[response_col] == 'Yes'].copy()\n",
        "df_filtered['Household Agreement Details'] = df_filtered.apply(get_relationships_and_gender, axis=1)\n",
        "\n",
        "# Flatten the list of tuples to get separate lists for roles and genders\n",
        "roles_list = []\n",
        "gender_list = []\n",
        "for details in df_filtered['Household Agreement Details'].dropna():\n",
        "    for role, gender in details:\n",
        "        roles_list.append(role)\n",
        "        gender_list.append(gender)\n",
        "\n",
        "# Convert to DataFrame for visualization\n",
        "df_roles = pd.DataFrame({'Household Role': roles_list, 'Gender': gender_list})\n",
        "\n",
        "# Step 4: Create a stacked bar chart for household roles disaggregated by gender\n",
        "plt.figure(figsize=(10, 5))\n",
        "df_pivot = df_roles.pivot_table(index='Household Role', columns='Gender', aggfunc='size', fill_value=0)\n",
        "\n",
        "# Plot as stacked bar chart\n",
        "df_pivot.plot(kind='barh', stacked=True, color={'Male': '#71b3ff', 'Female': '#e3555b'}, figsize=(10, 6))\n",
        "\n",
        "# Formatting\n",
        "plt.xlabel(\"Count\", fontsize=14)\n",
        "plt.ylabel(\"\")\n",
        "plt.title(\"Household Roles of Those Who Must Agree (Disaggregated by Gender)\", fontsize=14)\n",
        "plt.legend(title=\"Gender\", fontsize=12, title_fontsize=12, frameon=False, loc=\"upper right\")\n",
        "\n",
        "# Adjust layout and show plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "9XsISOyOpBY_",
        "outputId": "3e6c5bf1-bc98-4fc1-bea7-469015d6cb92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents who require household agreement: 23.3%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract relevant DataFrames\n",
        "df_main = df_cooking_survey['Spotlight Kampala Cooking Su...']\n",
        "df_roster = df_cooking_survey['household_roster_group']\n",
        "\n",
        "# Define relevant columns\n",
        "response_col = 'If you wanted to buy and use an electric appliance like an electric pressure cooker, are there people in your household who would need to agree?'\n",
        "agreement_col = 'Who in your household would need to agree?'\n",
        "\n",
        "# Step 1: Extract gender and household roles of those who need to agree\n",
        "def get_relationships_and_gender(row):\n",
        "    if pd.isna(row[agreement_col]):\n",
        "        return []\n",
        "\n",
        "    # Extract numeric indices from ${name_1} ${name_2} format\n",
        "    indices = [int(match) - 1 for match in re.findall(r'\\$\\{name_(\\d+)\\}', row[agreement_col])]\n",
        "\n",
        "    results = []\n",
        "    for idx in indices:\n",
        "        match = df_roster[(df_roster['_submission__id'] == row['_id'])].iloc[idx]  # Select nth row\n",
        "        if 'Relationship to the head of household' in match and 'Gender' in match:\n",
        "            results.append((match['Relationship to the head of household'], match['Gender']))\n",
        "\n",
        "    return results\n",
        "\n",
        "# Apply function to get relationships and gender\n",
        "df_filtered = df_main[df_main[response_col] == 'Yes'].copy()\n",
        "df_filtered['Household Agreement Details'] = df_filtered.apply(get_relationships_and_gender, axis=1)\n",
        "\n",
        "# Flatten the list of tuples to extract roles and genders\n",
        "roles_list = []\n",
        "gender_list = []\n",
        "for details in df_filtered['Household Agreement Details'].dropna():\n",
        "    for role, gender in details:\n",
        "        roles_list.append(role)\n",
        "        gender_list.append(gender)\n",
        "\n",
        "# Convert to DataFrame\n",
        "df_roles = pd.DataFrame({'Household Role': roles_list, 'Gender': gender_list})\n",
        "\n",
        "# Step 2: Calculate percentage of Male Heads of Household\n",
        "total_agreeing_people = len(df_roles)\n",
        "male_heads_count = len(df_roles[(df_roles['Household Role'] == 'Head of household') & (df_roles['Gender'] == 'Male')])\n",
        "\n",
        "# Compute percentage\n",
        "male_heads_percentage = (male_heads_count / total_agreeing_people) * 100 if total_agreeing_people > 0 else 0\n",
        "\n",
        "# Print result\n",
        "print(f\"Percentage of people who must agree that are male heads of household: {male_heads_percentage:.1f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5Bra3yLsbLS",
        "outputId": "92580c83-a813-4da2-ec2c-b23de77c08b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of people who must agree that are male heads of household: 74.2%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter for people who are NOT male heads of household\n",
        "non_male_heads_df = df_roles[\n",
        "    ~((df_roles['Household Role'] == 'Head of household') & (df_roles['Gender'] == 'Male'))\n",
        "].copy()\n",
        "\n",
        "# Display the list for inspection\n",
        "display(non_male_heads_df)\n"
      ],
      "metadata": {
        "id": "R4F6SV0sEkNh",
        "outputId": "95a9f2a0-3652-40ad-ac19-f0fba4e0bc30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       Household Role  Gender\n",
              "4    Mother or father  Female\n",
              "7     Husband or wife  Female\n",
              "9     Husband or wife  Female\n",
              "11  Head of household  Female\n",
              "13  Brother or sister    Male\n",
              "18    Husband or wife  Female\n",
              "22  Head of household  Female\n",
              "30  Head of household  Female"
            ],
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "        background-color: #E8F0FE;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('non_male_heads_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_02c99f4f-145b-4542-bac1-ec5678642764 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('non_male_heads_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "non_male_heads_df",
              "summary": "{\n  \"name\": \"non_male_heads_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Household Role\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Husband or wife\",\n          \"Brother or sister\",\n          \"Mother or father\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pWjoPPq1VQgR"
      },
      "source": [
        "# Income and expenses"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ejR25x0oacVd"
      },
      "source": [
        "## Total expense by source"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "id": "3yKTRzKgVTK8",
        "outputId": "7820295b-980f-4cfe-f43b-ed28379ea9e6"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "\"['Cooking fuels', 'Electricity'] not in index\"",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-457-935fa191bc03>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Start from expense_df and filter the relevant columns for plotting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mexpense_data_for_plot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpense_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexpense_labels_full\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'all'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Reorder columns as specified\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m specified_order = [\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4106\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4107\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4108\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_indexer_strict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4110\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   6198\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6200\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_if_missing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6202\u001b[0m         \u001b[0mkeyarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   6250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6251\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6252\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{not_found} not in index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6254\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['Cooking fuels', 'Electricity'] not in index\""
          ]
        }
      ],
      "source": [
        "# Start from expense_df and filter the relevant columns for plotting\n",
        "expense_data_for_plot = expense_df[expense_labels_full].dropna(how='all')\n",
        "\n",
        "# Reorder columns as specified\n",
        "specified_order = [\n",
        "    \"Food\", \"School fees\", \"Rent\", \"Transportation\", \"Healthcare\",\n",
        "    \"Loan repayment\", \"Business expenses\", \"Remittances\",\n",
        "    \"Entertainment\", \"Airtime and data\", \"Water\", \"Sanitation\",\n",
        "    \"Cooking fuels\", \"Electricity\"\n",
        "]\n",
        "expense_data_for_plot = expense_data_for_plot[specified_order]\n",
        "\n",
        "# Sort data by total expenses in descending order\n",
        "expense_data_for_plot['Total Expenses'] = expense_data_for_plot.sum(axis=1)\n",
        "expense_data_for_plot = expense_data_for_plot.sort_values('Total Expenses', ascending=False)\n",
        "expense_data_for_plot = expense_data_for_plot.drop(columns='Total Expenses')  # Drop the helper column\n",
        "\n",
        "# Calculate the average monthly spend across all respondents\n",
        "average_monthly_spend = expense_data_for_plot.sum(axis=1).mean()\n",
        "\n",
        "# Create the stacked bar chart with a horizontal line for the average monthly spend\n",
        "fig, ax = plt.subplots(figsize=(15, 8))\n",
        "expense_data_for_plot.plot(\n",
        "    kind='bar',\n",
        "    stacked=True,\n",
        "    ax=ax,\n",
        "    colormap='tab20',  # Use a more varied color scheme\n",
        "    legend=True\n",
        ")\n",
        "\n",
        "# Add a thin, solid black horizontal line at the average monthly spend\n",
        "ax.axhline(y=average_monthly_spend, color='black', linestyle='-', linewidth=1, label='Average monthly total')\n",
        "\n",
        "# Format primary y-axis for UGX\n",
        "ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f\"{int(x):,}\"))\n",
        "ax.tick_params(axis='y', labelsize=12)\n",
        "\n",
        "# Add a secondary y-axis for USD\n",
        "def ugx_to_usd(y):\n",
        "    return y / exchange_rate_ecooking * 1000\n",
        "\n",
        "def usd_to_ugx(y):\n",
        "    return y * exchange_rate_ecooking / 1000\n",
        "\n",
        "secax = ax.secondary_yaxis('right', functions=(ugx_to_usd, usd_to_ugx))\n",
        "secax.set_ylabel(\"Monthly expenses (USD/month)\", fontsize=14)\n",
        "secax.tick_params(axis='y', labelsize=14)\n",
        "\n",
        "# Remove the x-tick labels\n",
        "ax.set_xticks([])\n",
        "\n",
        "# Remove the title\n",
        "ax.set_title(\"\")\n",
        "\n",
        "# Update the legend\n",
        "legend = ax.legend(\n",
        "    title=\"Expense categories\",\n",
        "    fontsize=12,\n",
        "    loc='upper right',\n",
        "    title_fontsize=14,\n",
        "    frameon=False  # Remove the border around the legend\n",
        ")\n",
        "\n",
        "# Update x-axis and y-axis label fontsizes\n",
        "ax.set_xlabel(\"Respondents\", fontsize=14)\n",
        "ax.set_ylabel(\"Monthly expenses (1,000 UGX/month)\", fontsize=14)\n",
        "\n",
        "# Adjust layout and display the chart\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Print the average monthly spend\n",
        "print(f\"Average monthly total (UGX/month): {int(average_monthly_spend):,}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cI5XuXasafdp"
      },
      "source": [
        "## Average monthly expense by category"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825
        },
        "id": "-JooMSK-jxp6",
        "outputId": "d17b7a21-876f-42dd-cd5c-dd099bb16aca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average total monthly expense:  869.2188672086721\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Ensure \"Cooking fuels\" does not exist in expense_df\n",
        "expense_df_temp = expense_df\n",
        "\n",
        "if \"Cooking fuels\" in expense_df_temp.columns:\n",
        "    expense_df_temp.drop(columns=[\"Cooking fuels\"], inplace=True)\n",
        "\n",
        "# Combine \"Cooking fuels\" and \"Electricity\" into a new \"Energy\" category\n",
        "expense_df_temp['Energy'] = (\n",
        "    expense_df_temp[['Electricity', 'Average monthly firewood expense',\n",
        "                'Average monthly charcoal expense', 'Average monthly gas expense']].sum(axis=1)\n",
        ")\n",
        "\n",
        "# Remove the old fuel-related categories from the DataFrame\n",
        "expense_df_temp.drop(columns=['Electricity', 'Average monthly firewood expense',\n",
        "                         'Average monthly charcoal expense', 'Average monthly gas expense'], inplace=True)\n",
        "\n",
        "# Update expense labels\n",
        "updated_expense_labels = [\n",
        "    label for label in expense_labels_full\n",
        "    if label not in ['Electricity', 'Average monthly firewood expense',\n",
        "                     'Average monthly charcoal expense', 'Average monthly gas expense', 'Cooking fuels']\n",
        "]\n",
        "updated_expense_labels.append('Energy')  # Add new combined category\n",
        "\n",
        "# Final assertion to confirm \"Cooking fuels\" is gone\n",
        "assert \"Cooking fuels\" not in expense_df_temp.columns, \"Error: 'Cooking fuels' column still exists in expense_df_temp\"\n",
        "\n",
        "# Filter only relevant columns\n",
        "expense_data_for_plot = expense_df_temp[updated_expense_labels]\n",
        "\n",
        "# Calculate the percentage of each expense category relative to total expenses for each respondent\n",
        "expense_percentages = expense_data_for_plot.div(expense_data_for_plot.sum(axis=1), axis=0) * 100\n",
        "\n",
        "# Reorder columns based on the average percentage contribution in descending order\n",
        "ordered_columns = expense_percentages.mean().sort_values(ascending=True).index\n",
        "expense_percentages = expense_percentages[ordered_columns]\n",
        "expense_data_for_plot = expense_data_for_plot[ordered_columns]  # Reorder the total amounts similarly\n",
        "\n",
        "# Calculate the average total monthly expense (UGX) across all respondents\n",
        "average_monthly_expense = expense_data_for_plot.sum(axis=1).mean()\n",
        "print('Average total monthly expense: ', average_monthly_expense)\n",
        "\n",
        "# Create subplots\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(12, 8), gridspec_kw={'width_ratios': [1, 1]})\n",
        "\n",
        "# Boxplot for total amounts (left)\n",
        "expense_data_for_plot.boxplot(\n",
        "    vert=False,\n",
        "    patch_artist=True,\n",
        "    boxprops=dict(facecolor='lightblue'),\n",
        "    medianprops=dict(color='black', linewidth=1.5),\n",
        "    ax=axes[0]\n",
        ")\n",
        "axes[0].set_xlabel(\"Average monthly expense (UGX/month)\", fontsize=14)\n",
        "axes[0].set_ylabel(\"Expense categories\", fontsize=14)\n",
        "axes[0].set_xlim(0, 1000)\n",
        "axes[0].xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f\"{int(x):,}\"))\n",
        "\n",
        "# Set x-tick and y-tick label font sizes explicitly for left graph\n",
        "axes[0].tick_params(axis='x', labelsize=14)\n",
        "axes[0].tick_params(axis='y', labelsize=14)\n",
        "\n",
        "# Add secondary x-axis on the top for USD conversion\n",
        "secax = axes[0].secondary_xaxis('top', functions=(ugx_to_usd, usd_to_ugx))\n",
        "secax.set_xlabel(\"Average monthly expense (USD/month)\", fontsize=14)\n",
        "secax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f\"{x:.0f}\"))\n",
        "secax.tick_params(axis='x', labelsize=14)  # Ensure secondary x-axis ticks match font size\n",
        "\n",
        "# Boxplot for percentages (right)\n",
        "expense_percentages.boxplot(\n",
        "    vert=False,\n",
        "    patch_artist=True,\n",
        "    boxprops=dict(facecolor='lightblue'),\n",
        "    medianprops=dict(color='black', linewidth=1.5),\n",
        "    ax=axes[1]\n",
        ")\n",
        "axes[1].set_xlabel(\"Percent of monthly expenses\", fontsize=14)\n",
        "axes[1].set_ylabel(\"Expense categories\", fontsize=14)\n",
        "\n",
        "# Set x-tick and y-tick label font sizes explicitly for right graph\n",
        "axes[1].tick_params(axis='x', labelsize=14)\n",
        "axes[1].tick_params(axis='y', labelsize=14)\n",
        "axes[1].set_yticks([])  # Remove y-axis labels for the second plot\n",
        "axes[1].xaxis.set_major_formatter(mtick.PercentFormatter())\n",
        "\n",
        "# Remove gridlines\n",
        "for ax in axes:\n",
        "    ax.grid(False)\n",
        "\n",
        "# Adjust layout and save the figure\n",
        "plt.tight_layout()\n",
        "plt.savefig(fig_path + \"Monthly Expenses.png\", dpi=500)\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "nE08s8fbO32x",
        "outputId": "d2183eb5-fa25-4859-f3fd-2e3f18d4f470"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Sanitation             5.826667\n",
              "Water                 29.350913\n",
              "Entertainment         55.169231\n",
              "Airtime and data      38.567193\n",
              "Healthcare            76.561728\n",
              "Remittances           86.605691\n",
              "Energy                92.055285\n",
              "Transportation       167.530508\n",
              "School fees          169.721111\n",
              "Loan repayment       170.733333\n",
              "Rent                 141.666667\n",
              "Business expenses    181.428571\n",
              "Food                 338.295462\n",
              "dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Sanitation</th>\n",
              "      <td>5.826667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Water</th>\n",
              "      <td>29.350913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Entertainment</th>\n",
              "      <td>55.169231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Airtime and data</th>\n",
              "      <td>38.567193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Healthcare</th>\n",
              "      <td>76.561728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Remittances</th>\n",
              "      <td>86.605691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Energy</th>\n",
              "      <td>92.055285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Transportation</th>\n",
              "      <td>167.530508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>School fees</th>\n",
              "      <td>169.721111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loan repayment</th>\n",
              "      <td>170.733333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rent</th>\n",
              "      <td>141.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Business expenses</th>\n",
              "      <td>181.428571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Food</th>\n",
              "      <td>338.295462</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "display(expense_data_for_plot.mean(axis=0))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guRCIl6neEab"
      },
      "source": [
        "## Average monthly expense by category and connection type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9iBSht7eTDmh"
      },
      "outputs": [],
      "source": [
        "expense_labels_adapted = ['Airtime and data',\n",
        "                          'School fees',\n",
        "                          'Transportation',\n",
        "                          'Food',\n",
        "                          'Water',\n",
        "                          'Sanitation',\n",
        "                          'Rent',\n",
        "                          'Loan repayment',\n",
        "                          'Entertainment',\n",
        "                          'Healthcare',\n",
        "                          'Remittances',\n",
        "                          'Business expenses',\n",
        "                          'Energy']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKF_77hTY39a"
      },
      "source": [
        "# Seasonality"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "Qr24xaBUY6De",
        "outputId": "b2b45e32-e822-4a6b-c1de-b20164c72e98"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# List of month names from January to December\n",
        "month_labels = ['January', 'February', 'March', 'April', 'May', 'June',\n",
        "                'July', 'August', 'September', 'October', 'November', 'December']\n",
        "\n",
        "# Select columns excluding worst_outage_month_12\n",
        "worst_outage_month_cols = [f'worst_outage_month_{i}' for i in range(12)]\n",
        "\n",
        "# Count the number of '1s' in each column\n",
        "outage_counts = df_remote_power_quality_pre_survey[worst_outage_month_cols].sum()\n",
        "\n",
        "# Convert to percentage of total rows\n",
        "total_rows = len(df_remote_power_quality_pre_survey)\n",
        "outage_percentages = (outage_counts / total_rows) * 100\n",
        "\n",
        "# Create figure and axis\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Color gradient: Higher values get darker colors\n",
        "norm = plt.Normalize(vmin=outage_percentages.min(), vmax=outage_percentages.max())\n",
        "colors = plt.cm.Blues(norm(outage_percentages))\n",
        "\n",
        "# Plot bar chart\n",
        "bars = ax.bar(month_labels, outage_percentages, color=colors, edgecolor='black', linewidth=1.2)\n",
        "\n",
        "# Add integer labels on top of bars\n",
        "for bar, value in zip(bars, outage_percentages):\n",
        "    height = bar.get_height()\n",
        "    ax.text(bar.get_x() + bar.get_width()/2, height + 0.8, f'{int(round(value))}%',\n",
        "            ha='center', fontsize=11, fontweight='bold', color='black')\n",
        "\n",
        "# Formatting\n",
        "ax.set_xlabel('Month', fontsize=14)\n",
        "ax.set_ylabel('Percentage of reported worst outages (%)', fontsize=14)\n",
        "ax.set_title('In the last 12 months, were there any specific months\\nwhen there were especially many blackouts?',\n",
        "             fontsize=16, fontweight='bold')\n",
        "ax.set_xticks(range(len(month_labels)))\n",
        "ax.set_xticklabels(month_labels, rotation=45, ha='right', fontsize=12)\n",
        "ax.set_yticks(np.arange(0, int(outage_percentages.max()) + 10, 5))\n",
        "ax.set_ylim(0, outage_percentages.max() * 1.15)\n",
        "\n",
        "# Remove spines for cleaner look\n",
        "for spine in ['top', 'right']:\n",
        "    ax.spines[spine].set_visible(False)\n",
        "\n",
        "# Show the graph\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kSZpniAg7j-6"
      },
      "source": [
        "# Energy expenses"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ibcevz8ZNkej"
      },
      "outputs": [],
      "source": [
        "df_temp = df_cooking_survey['Spotlight Kampala Cooking Su...']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hVkq_j7eMuTn"
      },
      "source": [
        "## Frequency of fuel source purchases"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j653VuWYM0VD"
      },
      "source": [
        "## Fuel source costs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G5e61ZzrdHax",
        "outputId": "b6d21b23-0af5-4595-b39f-1eeb6cff46eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average percentage of total expenses spent on cooking fuels: 8.60%\n",
            "Average percentage of total expenses spent on electricity: 4.01%\n",
            "Average percentage of total expenses spent on energy (cooking fuels + electricity): 12.51%\n"
          ]
        }
      ],
      "source": [
        "# Sum relevant expense categories\n",
        "total_expenses = expense_df_duplicate[expense_labels_full].sum(axis=1, min_count=1)  # Corrected summation\n",
        "\n",
        "# Ensure necessary columns exist\n",
        "fuel_columns = ['Cooking fuels', 'Electricity']\n",
        "missing_columns = [col for col in fuel_columns if col not in expense_df.columns]\n",
        "\n",
        "if missing_columns:\n",
        "    raise KeyError(f\"Missing required columns: {missing_columns}\")\n",
        "\n",
        "# Create a temporary DataFrame to store energy-related calculations\n",
        "expense_df_temp_1 = expense_df.copy()\n",
        "\n",
        "# Calculate energy expenses (sum of Cooking fuels + Electricity), ignoring NaNs\n",
        "expense_df_temp_1['Energy'] = expense_df[fuel_columns].sum(axis=1, min_count=1)\n",
        "\n",
        "# Avoid division errors by ensuring only rows with valid total_expenses are considered\n",
        "valid_rows = total_expenses.notna() & (total_expenses > 0)\n",
        "\n",
        "# Compute percentages relative to total expenses, ignoring NaNs\n",
        "expense_df_temp_1.loc[valid_rows, 'Cooking fuels %'] = (expense_df['Cooking fuels'] / total_expenses) * 100\n",
        "expense_df_temp_1.loc[valid_rows, 'Electricity %'] = (expense_df['Electricity'] / total_expenses) * 100\n",
        "expense_df_temp_1.loc[valid_rows, 'Energy %'] = (expense_df_temp_1['Energy'] / total_expenses) * 100  # Combined energy percentage\n",
        "\n",
        "# Compute average percentages across valid respondents only\n",
        "avg_fuel_pct = expense_df_temp_1.loc[valid_rows, 'Cooking fuels %'].mean(skipna=True)\n",
        "avg_electricity_pct = expense_df_temp_1.loc[valid_rows, 'Electricity %'].mean(skipna=True)\n",
        "avg_energy_pct = expense_df_temp_1.loc[valid_rows, 'Energy %'].mean(skipna=True)\n",
        "\n",
        "# Print formatted results\n",
        "print(f\"Average percentage of total expenses spent on cooking fuels: {avg_fuel_pct:.2f}%\")\n",
        "print(f\"Average percentage of total expenses spent on electricity: {avg_electricity_pct:.2f}%\")\n",
        "print(f\"Average percentage of total expenses spent on energy (cooking fuels + electricity): {avg_energy_pct:.2f}%\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3od1NPwrta6_",
        "outputId": "64956087-f61b-4e75-bab6-18178104e513"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average monthly charcoal expense in UGX: 59209.41\n",
            "Average monthly electricity expense in UGX: 29683.33\n",
            "Average monthly charcoal expense in USD: $15.91\n",
            "Average monthly electricity expense in USD: $7.98\n"
          ]
        }
      ],
      "source": [
        "# Ensure required columns exist\n",
        "expense_columns = ['Electricity', 'Average monthly charcoal expense']\n",
        "missing_columns = [col for col in expense_columns if col not in expense_df.columns]\n",
        "\n",
        "if missing_columns:\n",
        "    raise KeyError(f\"Missing required columns: {missing_columns}\")\n",
        "\n",
        "# Compute the average monthly expense for charcoal and electricity (UGX)\n",
        "avg_charcoal_ugx = expense_df['Average monthly charcoal expense'].mean(skipna=True)\n",
        "avg_electricity_ugx = expense_df['Electricity'].mean(skipna=True)\n",
        "\n",
        "# Print the results\n",
        "print(f\"Average monthly charcoal expense in UGX: {avg_charcoal_ugx*1000:.2f}\")\n",
        "print(f\"Average monthly electricity expense in UGX: {avg_electricity_ugx*1000:.2f}\")\n",
        "\n",
        "# Convert to USD using the exchange rate\n",
        "avg_charcoal_usd = avg_charcoal_ugx / exchange_rate_ecooking * 1000\n",
        "avg_electricity_usd = avg_electricity_ugx / exchange_rate_ecooking * 1000\n",
        "\n",
        "# Print the results\n",
        "print(f\"Average monthly charcoal expense in USD: ${avg_charcoal_usd:.2f}\")\n",
        "print(f\"Average monthly electricity expense in USD: ${avg_electricity_usd:.2f}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "ptutmPSJ7oOZ",
        "outputId": "90326695-41e4-476c-ea23-5f46f0fb476e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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HXLx4Uern6uoKLy8vrFixAo8fP0bLli1x5MgR3LhxA4DiVeGFCxciPj4ebm5uGDlyJBo0aIDk5GScO3cOBw8eRHJycqlyqApdXV0sWrQIQ4cORfv27eHn54cHDx5Ij0+bPHlyicfU19dHgwYNsG3bNtStWxfm5uZwcXEp9h5rVfn7+2PAgAEAgLlz56q0z9SpU7Fjxw4MHDgQw4YNg6urK5KTkxEbG4t169ahcePGGDVqFMLCwhAQEIDff/8d9vb22LFjB44fP44VK1ZI9yyPGDECycnJ6NSpE2xtbXHv3j2EhoaiSZMm0u9ckyZNoK2tjUWLFiE1NRVyuRydOnWClZVVmeRAFcuWLYOHhweaNGmCgIAAVKtWDVevXkV4eDhsbGwwffp0qe+FCxfw0UcfoXv37mjbti3Mzc2RkJCAjRs34t9//8WKFSuUpr3HxcXh2rVryMnJwYMHD3Do0CEcOHAANWvWRGxsbJFT2YmISE00uqY6ERG9dXr37i309PRERkZGoX0CAgKErq6u9BiuvLw8YWdnJwCIefPmFbhPdna2WLRokXB2dhZyuVyYmZkJV1dXERISIlJTU6V+AMTYsWMLHOO7774TderUEXK5XDg5OYkNGzYU+DiojIwMMXbsWGFubi6MjIxE3759xfXr1wUAsXDhQoW+Dx48EGPHjhV2dnZCV1dXWFtbi86dO4vw8PBic1VQrHfu3BEAxJIlSxTaC3vM1LZt20TTpk2FXC4X5ubm4uOPPxb3799X6DNkyBBhaGiodPyCzv3EiRPC1dVVVKpUSeGxYCUZA4U8kiwrK0uYmZkJU1NThUegFefx48di3Lhxonr16qJSpUrC1tZWDBkyROExbg8ePBBDhw4VFhYWolKlSqJhw4Ziw4YNCuPs2LFDdO3aVVhZWYlKlSqJGjVqiNGjR4vExESFfuvXrxe1a9cW2traxT5arLDzL+h3sGbNmmLIkCEqnfOpU6dEr169hJmZmdDR0RHVq1cXI0aMUPrZPnjwQCxcuFC0b99e2NjYCB0dHWFmZiY6deokduzYodA3/3Fi+a9KlSoJa2tr8eGHH4qVK1cqPL6OiIjKl0yIUq7iQkRE9A45f/48mjZtis2bN3Mqbinl5OSgWrVq6N27N7777jtNh0NERFRh8B5vIiJ67xS0ovSKFSugpaWFdu3aaSCid8OuXbuQlJQEf39/TYdCRERUofAebyIieu8sXrwYv//+Ozp27AgdHR3p0VOjRo1SenQZFe/06dO4ePEi5s6di6ZNm0qLnREREdFLnGpORETvnQMHDiAkJARXrlxBeno6atSogcGDB2PmzJkqPQ+ZFAUEBGDz5s1o0qQJIiIiymyxNiIioncFC28iIiIiIiIiNeI93kRERERERERqxMKbiIiIiIiISI1YeBP9n6ysLHz++eeoVq0a9PX14ebmhgMHDmg6rArt7NmzGDduHJydnWFoaIgaNWrA29sbN27c0HRoFdbhw4chk8kKfJ06dUrT4VVoN2/ehK+vL2xtbWFgYAAnJyfMmTMHz54903RoFUJ6ejqCgoLQrVs3mJubQyaTISIiosC+V69eRbdu3WBkZARzc3MMHjwYSUlJ5RtwBaFK3vLy8hAREQFPT0/Y2dnB0NAQLi4umDdvHp4/f66ZwDVI1d+1gICAAj/rnJycyj/oCqAk/0ajoqLQsmVLVK5cGVWqVEH79u2xd+/e8g24AijJ3xn8XKOKjivIEP2fgIAA7NixA5MmTUKdOnUQERGBHj16ID4+Hu7u7poOr0JatGgRjh8/joEDB6JRo0b477//sGrVKnzwwQc4deoUF1gqwoQJE9C8eXOFNkdHRw1FU/H9888/aNGiBUxNTTFu3DiYm5vj5MmTCAoKwu+//46ffvpJ0yFq3KNHjzBnzhzUqFEDjRs3xuHDhwvsd//+fbRr1w6mpqaYP38+0tPTsXTpUly6dAlnzpxBpUqVyjdwDVMlb8+ePcPQoUPRsmVLBAYGwsrKSvr9+/XXX3Ho0CHIZLLyD15DVP1dAwC5XI5vv/1Woc3U1FTNEVZMquYtNDQUEyZMQM+ePbFw4UI8f/4cERER6NWrF6Kjo9G/f//yDVyDVP07g59r9FYQRCROnz4tAIglS5ZIbZmZmcLBwUG0atVKg5FVbMePHxdZWVkKbTdu3BByuVx8/PHHGoqqYouPjxcAxPbt2zUdylvlq6++EgDE5cuXFdr9/f0FAJGcnKyhyCqO58+fi8TERCGEEGfPnhUAxIYNG5T6ffLJJ0JfX1/cu3dPajtw4IAAIMLCwsor3ApDlbxlZWWJ48ePK+0bEhIiAIgDBw6UR6gVhqq/a0OGDBGGhoblHF3FpWre6tSpI5o3by7y8vKkttTUVGFkZCQ8PT3LK9wKQdW/M/i5Rm8DTjUnArBjxw5oa2tj1KhRUpuenh6GDx+OkydP4p9//tFgdBVX69atlb5FrlOnDpydnXH16lUNRfX2ePr0KXJycjQdxlshLS0NAFC1alWFdhsbG2hpafFqBl5eWbS2ti62X3R0NHr16oUaNWpIbV26dEHdunURFRWlzhArJFXyVqlSJbRu3VqpvV+/fgDw3n3eqfq7li83N1f6N/w+UzVvaWlpsLKyUphFYWJiAiMjI+jr66szxApH1b8z+LlGbwMW3kQA/vjjD9StWxcmJiYK7S1atAAAnD9/XgNRvZ2EEHjw4AEsLCw0HUqFNnToUJiYmEBPTw8dO3bEb7/9pumQKrQOHToAAIYPH47z58/jn3/+wbZt27B27VpMmDABhoaGmg3wLZGQkICHDx+iWbNmSttatGiBP/74QwNRvb3+++8/AODnXRGePXsGExMTmJqawtzcHGPHjkV6erqmw6rQOnTogH379iE0NBR3797FtWvXMHbsWKSmpmLixImaDk/jXv87g59r9LbgPd5EABITE2FjY6PUnt/277//lndIb63IyEgkJCRgzpw5mg6lQqpUqRK8vLzQo0cPWFhY4MqVK1i6dCnatm2LEydOoGnTppoOsULq1q0b5s6di/nz5yM2NlZqnzlzJubNm6fByN4uiYmJAFDo511ycjKysrIgl8vLO7S30uLFi2FiYoLu3btrOpQKycbGBtOmTcMHH3yAvLw87Nu3D2vWrMGFCxdw+PBh6Ojwz9CCfPPNN3j06BEmTJiACRMmAHj55c6vv/6KVq1aaTg6zXv97wx+rtHbgp94RAAyMzML/EDW09OTtlPx8r+Vb9WqFYYMGaLpcCqk1q1bK0xZ9fT0xIABA9CoUSNMnz4d+/bt02B0FZu9vT3atWsHLy8vVKlSBXv37sX8+fNhbW2NcePGaTq8t0L+Z1lxn3f8A7V48+fPx8GDB7FmzRpUrlxZ0+FUSAsWLFB47+vri7p162LmzJnYsWMHfH19NRRZxWZgYIB69erB1tYWvXr1wtOnT7F8+XL0798fR48efa8X4izo7wx+rtHbgoU3EQB9fX1kZWUptec/JuZ9u6eqNP777z/07NkTpqam0j3zpBpHR0f06dMHMTExyM3NZe4KsHXrVowaNQo3btyAra0tAKB///7Iy8vD559/Dj8/P1SpUkXDUVZ8+Z9l/Lx7M9u2bcOsWbMwfPhwfPLJJ5oO560yefJkfPnllzh48CAL70IMHDgQOjo62L17t9TWp08f1KlTBzNnzsS2bds0GJ3mFPZ3Bj/X6G3Be7yJ8HIqUv5UpVflt1WrVq28Q3qrpKamonv37khJScG+ffuYr1Kws7NDdnY2MjIyNB1KhbRmzRo0bdpUKrrzeXp64tmzZ7yHT0X5UzEL+7wzNzfnVaFiHDhwAP7+/ujZsyfWrVun6XDeOvr6+qhSpQqSk5M1HUqF9Ndff2Hfvn3w9PRUaDc3N4e7uzuOHz+uocg0q6i/M/i5Rm8LFt5EAJo0aYIbN24orbp6+vRpaTsV7Pnz5+jduzdu3LiBPXv2oEGDBpoO6a30119/QU9PD0ZGRpoOpUJ68OABcnNzldpfvHgBAFwdXkXVq1eHpaVlgYv5nTlzhp91xTh9+jT69euHZs2aISoqivcol8LTp0/x6NEjWFpaajqUCunBgwcAUOjn3fv4WVfc3xn8XKO3BQtvIgADBgxAbm4uwsPDpbasrCxs2LABbm5usLOz02B0FVdubi58fHxw8uRJbN++nYu+qCApKUmp7cKFC4iNjUXXrl2hpcWP5YLUrVsXf/zxB27cuKHQ/uOPP0JLSwuNGjXSUGRvHy8vL+zZs0fhMYm//vorbty4gYEDB2owsort6tWr6NmzJ+zt7bFnzx5OXS3G8+fP8fTpU6X2uXPnQgiBbt26aSCqis/R0RFaWlrYtm0bhBBS+/3793H06NH3bgFOVf/O4OcavQ34VS0RADc3NwwcOBDTp0/Hw4cP4ejoiI0bN+Lu3bv47rvvNB1ehfXZZ58hNjYWvXv3RnJyMjZv3qywfdCgQRqKrOLy8fGBvr4+WrduDSsrK1y5cgXh4eEwMDDAwoULNR1ehTV16lTExcWhbdu2GDduHKpUqYI9e/YgLi4OI0aM4O0N/2fVqlVISUmRnsSwe/du3L9/HwAwfvx4mJqaYsaMGdi+fTs6duyIiRMnIj09HUuWLEHDhg0xdOhQTYavMcXlTUtLCx4eHnjy5AmmTp2KvXv3Kuzv4ODw3n3xWFzOnjx5gqZNm8LPzw9OTk4AgP379+Pnn39Gt27d0KdPH43FrknF5c3S0hLDhg3Dt99+i86dO6N///54+vQp1qxZg8zMTEyfPl2T4Zc7Vf/O4OcavRUEEQkhhMjMzBRTpkwR1tbWQi6Xi+bNm4t9+/ZpOqwKrX379gJAoS9StnLlStGiRQthbm4udHR0hI2NjRg0aJC4efOmpkOr8E6fPi26d+8urK2tha6urqhbt6746quvxIsXLzQdWoVRs2bNQv893rlzR+p3+fJl0bVrV2FgYCAqV64sPv74Y/Hff/9pLnANKy5vd+7cKfKzbsiQIZo+hXJXXM6ePHkiBg0aJBwdHYWBgYGQy+XC2dlZzJ8/X2RnZ2s6fI1R5d/oixcvRGhoqGjSpIkwMjISRkZGomPHjuLQoUOaDV4DSvJ3Bj/XSg6ACAoK0nQY7w2ZEK/MYyEiIiIiIqJyExERUeiV+c8//1xtM+JkMhmCgoIQHByslvFJEaeaExERERERadicOXNQq1YthTYXFxcNRUNljYU3ERERERGRhnXv3h3NmjXTdBgVkhACz58/f6sXtuTyuURERERERBWYTCYrcEq4vb09AgICFNpSUlIwadIk2NnZQS6Xw9HREYsWLUJeXl6pjh0aGgpnZ2cYGBjAzMwMzZo1w5YtWxT6/PHHH+jevTtMTExgZGSEzp0749SpUwp9goODIZPJlMaPiIiATCbD3bt3Fc6rV69e2L9/P5o1awZ9fX2EhYVJ5zd58mTY29tDLpfD1tYW/v7+ePTokbR/VlYWgoKC4OjoCLlcDjs7O0ybNg1ZWVmlykFZ4BVvIiIiIiIiDUtNTVUoHgHAwsKiRGM8e/YM7du3R0JCAkaPHo0aNWrgxIkTmD59OhITE7FixYoSjbd+/XpMmDABAwYMwMSJE/H8+XNcvHgRp0+fxkcffQQA+PPPP9G2bVuYmJhg2rRp0NXVRVhYGDp06IAjR47Azc2tRMfMd/36dfj5+WH06NEYOXIk6tWrh/T0dLRt2xZXr17FsGHD8MEHH+DRo0eIjY3F/fv3YWFhgby8PHh6euLYsWMYNWoU6tevj0uXLmH58uW4ceMGdu3aVap43hQLbyIiIiIiIg3r0qWLUltJ18FetmwZbt++jT/++AN16tQBAIwePRrVqlXDkiVL8Nlnn8HOzk7l8fbu3QtnZ2ds37690D6zZs3CixcvcOzYMdSuXRsA4O/vj3r16mHatGk4cuRIic4h361bt7Bv3z54eHhIbUFBQbh8+TJiYmLQr18/hRjyc7VlyxYcPHgQR44cgbu7u9THxcUFgYGBOHHiBFq3bl2qmN4Ep5oTERERERFp2OrVq3HgwAGFV0lt374dbdu2hZmZGR49eiS9unTpgtzcXPzvf/8r0XiVK1fG/fv3cfbs2QK35+bm4pdffkHfvn2lohsAbGxs8NFHH+HYsWNIS0sr8XkAQK1atRSKbgCIjo5G48aNFYrufPnT2Ldv34769evDyclJIQedOnUCAMTHx5cqnjfFwpvoNVlZWQgODtboPSBvI+at5Jiz0mHeSod5KznmrHSYt9Jh3krnXcpbixYt0KVLF4VXSd28eRP79u2DpaWlwit/rIcPH5ZovM8//xxGRkZo0aIF6tSpg7Fjx+L48ePS9qSkJDx79gz16tVT2rd+/frIy8vDP//8U+LzAKC0wjsA3L59u9iV3m/evIk///xTKQd169YFUPIclBUW3kSvycrKQkhIyDvxAV6emLeSY85Kh3krHeat5Jiz0mHeSod5K533PW+5ubkK7/Py8vDhhx8qXTnPf3l5eZVo/Pr16+P69evYunUr3N3dER0dDXd3dwQFBZU41oIWVivoHPKVdgXzvLw8NGzYsNAcjBkzplTjvine401ERERERFSBmZmZISUlRaEtOzsbiYmJCm0ODg5IT08v1dXywhgaGsLHxwc+Pj7Izs5G//798dVXX2H69OmwtLSEgYEBrl+/rrTftWvXoKWlJd1TbmZmBuDlquSVK1eW+t27d0/lWBwcHHD58uVi+1y4cAGdO3cutNjXBF7xJiIiIiIiqsAcHByU7s8ODw9Xulrs7e2NkydPYv/+/UpjpKSkICcnp0THffz4scL7SpUqoUGDBhBC4MWLF9DW1kbXrl3x008/KTwO7MGDB9iyZQvc3d1hYmIinQMAhfPIyMjAxo0bVY7Hy8sLFy5cwM6dO5W25S+u5u3tjYSEBKxfv16pT2ZmJjIyMlQ+XlniFW+i1+Q/4zA1NVXDkbxd8hfOKO0CGu8j5qx0mLfSYd5KjjkrHeatdJi30lFn3oyNjSvMFdMRI0YgMDAQXl5e+PDDD3HhwgXs379f6XFjU6dORWxsLHr16oWAgAC4uroiIyMDly5dwo4dO3D37t0SPaKsa9eusLa2Rps2bVC1alVcvXoVq1atQs+ePWFsbAwAmDdvHg4cOAB3d3eMGTMGOjo6CAsLQ1ZWFhYvXqwwVo0aNTB8+HBMnToV2tra+P7772FpaYm///5bpXimTp2KHTt2YODAgRg2bBhcXV2RnJyM2NhYrFu3Do0bN8bgwYMRFRWFwMBAxMfHo02bNsjNzcW1a9cQFRUlPRu83AkiUnD79m0BgC+++OKLL7744ouv9/iVmppaLn97btiwQQAQZ8+eLbRPbm6u+Pzzz4WFhYUwMDAQHh4e4tatW6JmzZpiyJAhCn2fPn0qpk+fLhwdHUWlSpWEhYWFaN26tVi6dKnIzs6W+gEQQUFBRcYWFhYm2rVrJ6pUqSLkcrlwcHAQU6dOVcrNuXPnhIeHhzAyMhIGBgaiY8eO4sSJE0rj/f7778LNzU1UqlRJ1KhRQyxbtkw6/zt37kj9atasKXr27FlgTI8fPxbjxo0T1atXF5UqVRK2trZiyJAh4tGjR1Kf7OxssWjRIuHs7CzkcrkwMzMTrq6uIiQkpNx+rq+TCVHCh8MRveNSU1NRuXJl/PPPP9LUGCIiIiJ6v1SkK9709uNUc6LX5H/AmpiYsPAmIiIiIqI3xsXViIiIiIiIiNSIhTcRERERERGRGrHwJiIiIiIiIlIjFt5EREREREREasTCm4iIiIiIiEiNWHgTERERERERqRELbyIiIiIiIiI1YuFNREREREREpEYsvImIiIiIiIjUiIU3ERERERERAQBkMhmCg4M1HYaCgIAA2Nvbl3g/e3t7BAQElHk8paGj6QCIKqr+fftDR4f/RIiIiEhzzMzN8OPWHzUdBr0DIiIiMHTo0EK3nzx5Ei1btlRrDM+ePcPixYvRoUMHdOjQQa3HKsiVK1cQFRVV6kL+TbCqICrEwjkLYGRkpOkwiIiI6D02adpkTYdA75g5c+agVq1aSu2Ojo5qP/azZ88QEhICACUqvNevX4+8vLwSH+/69evQ0vr/k7yvXLmCkJAQdOjQgYU3ERERERHR26Sv92D89yi13I5nbWGKXVGbSrVv9+7d0axZszKOSD0yMjJgaGgIXV3dUu0vl8vLOKLSY+FNRERERET0Bv57lIrWn/5Ubsc7saxPuR0LABISEvDll19i7969SElJgaOjIz777DMMGzZMod/z58+xcOFCbNmyBX///TfMzMzQqlUrLFmyBNra2tKV9pCQEOnKd1BQEIKDgxEQEIAdO3bgwoULGD9+PI4ePYrOnTtj165dCAgIwOHDh3H37l3pWHl5eQgNDcW3336LmzdvwtjYGK6urpg3b570xYK9vT06dOiAiIgIhan2HTt2lMaJj4/Hhg0bsHfvXiQmJioV+V27dsW9e/dw/fr1N8ohC28iIiIiIqL3RGpqKh49eqTQJpPJUKVKlQL7P3jwAC1btoRMJsO4ceNgaWmJuLg4DB8+HGlpaZg0aRIAIDc3F7169cKvv/4KX19fTJw4EU+fPsWBAwdw+fJldOnSBWvXrsUnn3yCfv36oX///gCARo0aScfKycmBh4cH3N3dsXTpUhgYGBR6HsOHD0dERAS6d++OESNGICcnB0ePHsWpU6cKvKLfrl07TJgwAd988w1mzJiB+vXrAwDq16+PwYMH44cffsD+/fvRq1cvaZ///vsPhw4dQlBQkGrJLQILbyIiIiIiovdEly5dlNrkcjmeP39eYP+ZM2ciNzcXly5dkorzwMBA+Pn5ITg4GKNHj4a+vj5++OEH/Prrr1i2bBkmT/7/axN88cUXEEJAJpNhwIAB+OSTT9CoUSMMGjRI6VhZWVkYOHAgFixYUOQ5xMfHIyIiAhMmTMDKlSul9s8++wxCiAL3qV27Ntq2bYtvvvkGH374ocI95paWlrC1tcXmzZsVCu8ff/wReXl5BcZaUiy8iYiIiIiI3hOrV69G3bp1Fdq0tbUL7CuEQHR0NLy9vSGEULhS7uHhga1bt+LcuXNo06YNoqOjYWFhgfHjxyuNI5PJVI7vk08+KbZPdHQ0ZDJZgVeiS3KsfFpaWvj444/xzTff4OnTpzA2NgYAREZGonXr1gUuRldSLLyJiIiIiIjeEy1atFB5cbWkpCSkpKQgPDwc4eHhBfZ5+PAhAOD27duoV6/eGz2OV0dHB7a2tsX2u337NqpVqwZzc/NSH+t1/v7+WLRoEXbu3Al/f39cv34dv//+O9atW1cm47PwJiIiIiIiIiX5j/AaNGgQhgwZUmCfV+/RflNyuVzh8V/lqUGDBnB1dcXmzZvh7++PzZs3o1KlSvD29i6T8Vl4ExERERERkRJLS0sYGxsjNze3wHvDX+Xg4IDTp0/jxYsXhT7+qzTTwAs71v79+5GcnFyiq97FHd/f3x+ffvopEhMTsWXLFvTs2RNmZmZvGi4AQDNfJxARERERFeNhUhJWrVuFh0lJmg6FylliYiKCg4ORmJio6VDea9ra2vDy8kJ0dDQuX76stD3plX+bXl5eePToEVatWqXUL3/Bs/xVylNSUt4oLi8vLwghpEeSFXSsghgaGhZ5fD8/P8hkMkycOBF//fVXmSyqlo9XvImIiIioQkp6lITVYWvQsX0nWFlaajocKkeJiYkICQmBp6cnbGxsNB3OOyUuLg7Xrl1Tam/dujVq166t1L5w4ULEx8fDzc0NI0eORIMGDZCcnIxz587h4MGDSE5OBvDyavEPP/yATz/9FGfOnEHbtm2RkZGBgwcPYsyYMejTpw/09fXRoEEDbNu2DXXr1oW5uTlcXFzg4uJSonPo2LEjBg8ejG+++QY3b95Et27dkJeXh6NHj6Jjx44YN25cgfs1adIE2traWLRoEVJTUyGXy9GpUydYWVkBeHmFv1u3bti+fTsqV66Mnj17liiuorDwJiIiIiIiegPWFqY4saxPuR6vtGbPnl1g+4YNGwosvKtWrYozZ85gzpw5iImJwZo1a1ClShU4Oztj0aJFUj9tbW38/PPP+Oqrr7BlyxZER0ejSpUqcHd3R8OGDaV+3377LcaPH4/JkycjOzsbQUFBJS688+Nt1KgRvvvuO0ydOhWmpqZo1qwZWrduXeg+1tbWWLduHRYsWIDhw4cjNzcX8fHxUuENvPwCYc+ePfD29oZcLi9xXIWRiaKuxRO9h9LS0mBqaoqzR8/AyMhI0+EQERG9t/68egUDPhqAHVt2wLl+A02HoxGTpk3Gvl/2aTqMcnfu3Dm4urri999/xwcffKDpcOg98tNPP6Fv37743//+h7Zt25bZuLzHm4iIiIiIiAjA+vXrUbt2bbi7u5fpuJxqTkREREQV2vOs53iW+UzTYWhETm4OMjIyNB1GucvMzNR0CPSe2bp1Ky5evIi9e/di5cqVZbYCez4W3kRERERUoQ0aWnYrC7+NeOsbkfr5+fnByMgIw4cPx5gxY8p8fBbeRERERERE9F5T99JnLLyJiIiIqELbvGEz6tdz0nQYGjFt1uf4KfYnTYdR7s6fP1/m99gSaRILbyIiIiKq0PTkejDQN9B0GBqho60DQ0NDTYdR7vT19TUdAlGZ4qrmRERERERERGrEwpuIiIiIiIhIjVh4ExEREREREakRC28iIiIiIiIiNWLhTUREREQVkqWFJcaOHgNLC0tNh0LlzMbGBkFBQbCxsdF0KO8lmUyGcePGaToMtYmIiIBMJsPdu3fL7ZgsvImIiIioQrKytMS4wHGwsmTh/b6xsbFBcHAwC281uH37NkaPHo3atWtDT08PJiYmaNOmDVauXInMzExNh/fO4uPEiIiIiIiI3gN79+7FwIEDIZfL4e/vDxcXF2RnZ+PYsWOYOnUq/vzzT4SHh2s6zHcSC28iIiIiIqI34OfrhyfJT8rteGbmZvhx648l2ufOnTvw9fVFzZo1cejQIYXZBGPHjsWtW7ewd+/esg61UBkZGe/VM+pZeBMREREREb2BJ8lPsGLx8nI73qRpk0u8z+LFi5Geno7vvvuuwCn8jo6OmDhxokLbrl27MGvWLNy8eROOjo74+uuv0a1bN2n7vXv3sGjRIvz666/4+++/YWBggE6dOmHJkiWwt7eX+kVERGDo0KE4fPgwtm3bhh07duDFixd48uTllxVxcXFYuHAhzp07B5lMhnr16mHy5Mn46KOPpDG2b9+OhQsX4sqVKzA0NES3bt2waNEiVK9eXepz8eJFLFu2DP/73//w77//onLlyujRoweWLFmCKlWqlDhnZemdvcf77t27kMlkiIiIkNqCg4Mhk8lKNZ5MJkNwcHCx/d7kGKo6fPgwZDIZDh8+rNbjvCvK42dCRERERFSR7d69G7Vr10br1q1V6n/s2DGMGTMGvr6+WLx4MZ4/fw4vLy88fvxY6nP27FmcOHECvr6++OabbxAYGIhff/0VHTp0wLNnz5TGHDNmDK5cuYLZs2fjiy++APCyKO/ZsyeSk5Mxffp0LFy4EE2aNMG+ffuk/SIiIuDt7Q1tbW0sWLAAI0eORExMDNzd3ZGSkiL1O3DgAP766y8MHToUoaGh8PX1xdatW9GjRw8IIUqZubJRIa54538DcvbsWTRr1kzT4VApzJ8/Hw0aNEDfvn01HQoREREREb0iLS0NCQkJ6NOnj8r7XL16FVeuXIGDgwMAoGPHjmjcuDF+/PFHacXznj17YsCAAQr79e7dG61atUJ0dDQGDx6ssM3c3By//vortLW1AQCpqamYMGECWrRogcOHD0NPT0/qm18ov3jxAp9//jlcXFzwv//9T+rj7u6OXr16Yfny5QgJCQHwsrD/7LPPFI7ZsmVL+Pn54dixY2jbtq3K51/W3tkr3gWZNWtWqVfqy8zMxKxZs8o4onfH/PnzsWvXLk2HQUREREREr0lLSwMAGBsbq7xPly5dpKIbABo1agQTExP89ddfUpu+vr703y9evMDjx4/h6OiIypUr49y5c0pjjhw5Uiq6gZdXqJ8+fYovvvhCoegGIM1Y/e233/Dw4UOMGTNGoU/Pnj3h5OSkcF/6q/E8f/4cjx49QsuWLQGgwHjK03tVeOvo6Cj9QFWlp6cHHZ0KMUHgvfH8+XPk5eVpOgwiIiIioreaiYkJAODp06cq71OjRg2lNjMzM+m+bODlxcnZs2fDzs4OcrkcFhYWsLS0REpKClJTU5X2r1WrlsL727dvAwBcXFwKjePevXsAgHr16iltc3JykrYDQHJyMiZOnIiqVatCX18flpaW0jELiqc8VdjCOyAgAEZGRkhISEDfvn1hZGQES0tLTJkyBbm5uQp9U1JSEBAQAFNTU1SuXBlDhgxRmOuf7/V7fV1cXNCxY0elfnl5eahevbrCtImC7vE+duwYmjdvDj09PTg4OCAsLExprILuNS9szHv37mHMmDGoV68e9PX1UaVKFQwcOLDUD3bPP98bN25g0KBBMDU1haWlJb788ksIIfDPP/+gT58+MDExgbW1Nb7++mulMbKyshAUFARHR0fI5XLY2dlh2rRpyMrKUjiPjIwMbNy4ETKZDDKZDAEBAdL2hIQEDBs2DFWrVoVcLoezszO+//57hePk37e+detWzJo1C9WrV4eBgYH07dzp06fRrVs3mJqawsDAAO3bt8fx48eV4lXlZ0JERERE9D4xMTFBtWrVcPnyZZX3efXK9KtevVd6/Pjx+Oqrr+Dt7Y2oqCj88ssvOHDgAKpUqVLgBbRXr0irg7e3N9avX4/AwEDExMTgl19+ke4V1/QFvQp9CTc3NxceHh5wc3PD0qVLcfDgQXz99ddwcHDAJ598AuDlD75Pnz44duwYAgMDUb9+fezcuRNDhgwpdnwfHx8EBwfjv//+g7W1tdR+7Ngx/Pvvv/D19S1030uXLqFr166wtLREcHAwcnJyEBQUhKpVq5b6fF9dnMDW1hZ3797F2rVr0aFDB1y5cgUGBgalGtfHxwf169fHwoULsXfvXsybNw/m5uYICwtDp06dsGjRIkRGRmLKlClo3rw52rVrB+DlL6enpyeOHTuGUaNGoX79+rh06RKWL1+OGzduSFPLN23ahBEjRqBFixYYNWoUAEjTUh48eICWLVtCJpNh3LhxsLS0RFxcHIYPH460tDRMmjRJIda5c+eiUqVKmDJlCrKyslCpUiUcOnQI3bt3h6urK4KCgqClpYUNGzagU6dOOHr0KFq0aAGg9D+TrKwshS8S8ot9IiIiIqJ3Ra9evRAeHo6TJ0+iVatWZTLmjh07MGTIEIULeM+fPy/wImhB8muGy5cvw9HRscA+NWvWBABcv34dnTp1Uth2/fp1afuTJ0/w66+/IiQkBLNnz5b63Lx5U+XzUacKXXg/f/4cPj4++PLLLwEAgYGB+OCDD/Ddd99JhXdsbCz+97//YfHixZg6dSoA4JNPPinwSvbrfHx8MHv2bOzYsUNaIAAAtm3bBiMjI/Ts2bPQfWfPng0hBI4ePSpNw/Dy8kLDhg1Lfb4lXZxAVS1atJCu/I4aNQr29vb47LPPsGDBAnz++ecAAD8/P1SrVg3ff/+9VHhv2bIFBw8exJEjR+Du7i6N5+LigsDAQJw4cQKtW7fGoEGDEBgYiNq1a2PQoEEKx545cyZyc3Nx6dIlaQn/wMBA+Pn5ITg4GKNHj1a6F+O3336T2oQQCAwMRMeOHREXFyfNWBg9ejScnZ0xa9Ys/PLLLwBK/zNZsGCBtCDDqz4ePqTQb/qIiKji0NbWQvUCHo1D9C4wMzfTdAj0jpg2bRoiIyMxYsQIHDp0SOni1O3bt7Fnzx6lR4oVRVtbW2m18NDQUKUZyoXp2rUrjI2NsWDBAnTr1k1pcTWZTIZmzZrBysoK69atw7BhwyCXywG8fATZ1atXpSI7/+/21+NZsWKFyuejThW68AZeFmmvatu2LTZt2iS9//nnn6GjoyMV4sDLpI8fPx5Hjx4tcuy6deuiSZMm2LZtm1R45+bmYseOHejdu3ehUyFyc3Oxf/9+9O3bV+Heh/r168PDwwM///xzic8TUF6cIC0tTWFxgtIW3iNGjJD+W1tbG82aNcP9+/cxfPhwqb1y5cqoV6+ewmIJ27dvR/369eHk5IRHjx5J7fnfNMXHxxf5OAIhBKKjo+Ht7Q0hhMIYHh4e2Lp1K86dO4c2bdpI7UOGDFHIw/nz53Hz5k3MmjVL4dEFANC5c2ds2rQJeXl5EEKU+mcyffp0fPrpp9L7tLQ02NnZocvck5AbmBS6HxERVQwnlvXBvl9iNR0GEVGF5uDggC1btkizYf39/eHi4oLs7GycOHEC27dvV7hdVBW9evXCpk2bYGpqigYNGuDkyZM4ePCgys/MNjExwfLlyzFixAg0b94cH330EczMzHDhwgU8e/YMGzduhK6uLhYtWoShQ4eiffv28PPzw4MHD7By5UrY29tj8uTJ0ljt2rXD4sWL8eLFC1SvXh2//PIL7ty5U9JUqUWFLrz19PRgaWmp0Pb6Df337t2DjY0NjIyMFPoVdPN9QXx8fDBjxgwkJCSgevXqOHz4MB4+fAgfH59C90lKSkJmZibq1KmjtK1evXqlLrwzMzOxYMECbNiwAQkJCQrf1rzJYgCvL4xgamoKPT09WFhYKLW/WtzevHkTV69eVfoZ5Hv48GGRx01KSkJKSgrCw8MRHh6u0hivL7iQPzWkqFsHUlNTkZWVVeqfiVwul745IyIiIiIqKTNzM0yaNrlcj1canp6euHjxIpYsWYKffvoJa9euhVwuR6NGjfD1119j5MiRJRpv5cqV0NbWRmRkJJ4/f442bdrg4MGD8PDwUHmM4cOHw8rKCgsXLsTcuXOhq6sLJycnqaAGXq7/ZWBggIULF+Lzzz+HoaEh+vXrh0WLFqFy5cpSvy1btmD8+PFYvXo1hBDo2rUr4uLiUK1atRKdlzpU6MK7PKb5+vj4YPr06di+fTsmTZqEqKgomJqaolu3bmUy/quLub2qoOkX48ePx4YNGzBp0iS0atUKpqamkMlk8PX1faPFAArKoyqLJeTl5aFhw4ZYtmxZgX3t7OyKPG5+zIMGDSq0cG7UqJHC+9dnGeSPsWTJEjRp0qTAMYyMjBTu0SYiIiIiKk8/bv1R0yGorE6dOoVeFMv3+nTtfK8v+ly5cmWlRZML6hcQEFDk1fTevXujd+/eRcbk7e0Nb2/vIvtUr14dMTExSu2vn09x8ahDhS68VVGzZk38+uuvSE9PV7jqff36dZX2r1WrFlq0aCFNN4+JiUHfvn2LvAJqaWkJfX39Am/Uf/24ZmYvv416fYGBV5e9z/emixOUNQcHB1y4cAGdO3cu9AuEfAVtt7S0hLGxMXJzc9GlS5dSxwC8nDpS1Bgl+ZkQERERERGVpwr7ODFV9ejRAzk5OVi7dq3Ulpubi9DQUJXH8PHxwalTp/D999/j0aNHRU4zB15eLfbw8MCuXbvw999/S+1Xr17F/v37FfqamJjAwsIC//vf/xTa16xZU+C4b7I4QVnz9vZGQkIC1q9fr7QtMzMTGRkZ0ntDQ0OlLwi0tbXh5eWF6OjoAh9dkJSUVGwMrq6ucHBwwNKlS5Genl7oGCX5mRAREREREZWnt/6Kd+/evdGmTRt88cUXuHv3Lho0aICYmJgS3RPt7e2NKVOmYMqUKTA3N1fp6mxISAj27duHtm3bYsyYMcjJyUFoaCicnZ1x8eJFhb4jRozAwoULMWLECDRr1gz/+9//cOPGDaUx33RxgrI2ePBgREVFITAwEPHx8WjTpg1yc3Nx7do1REVFYf/+/WjWrBmAlwXywYMHsWzZMlSrVg21atWCm5sbFi5ciPj4eLi5uWHkyJFo0KABkpOTce7cORw8eBDJyclFxqClpYVvv/0W3bt3h7OzM4YOHYrq1asjISEB8fHxMDExwe7duwGU7GdCRERERERUXt76wltLSwuxsbGYNGkSNm/eDJlMBk9PT3z99ddo2rSpSmPY2tqidevWOH78OEaMGAFdXd1i92nUqBH279+PTz/9FLNnz4atrS1CQkKQmJioVOTNnj0bSUlJ2LFjB6KiotC9e3fExcXByspKoV9ZLE5QlrS0tLBr1y4sX74cP/zwA3bu3AkDAwPUrl0bEydORN26daW+y5Ytw6hRozBr1ixkZmZiyJAhcHNzQ9WqVXHmzBnMmTMHMTExWLNmDapUqQJnZ2csWrRIpTg6dOiAkydPYu7cuVi1ahXS09NhbW0NNzc3jB49WupXkp8JERERERFReZGJwu6cJ3pPpaWlwdTUFGO2pfBxYkREb4ETy/rg1CE+ToyIiCqut/4ebyIiIiIiIqKKjIU3ERERKUhPTsTJLcFIT07UdCjvnMTERAQHByMxkbklInqfsPAmIiIiBRnJiTj14xxksPAuc4mJidL6I0RE9P5g4U1ERERERESkRiy8iYiIiIiIiNSIhTcRERERERGRGr31z/EmIiIi9cjJzsSL5xmaDqNYubk5yMio+HECQGZmpqZDICIiDWDhTURERAWK+rytpkNQmZGRkaZDICIiKhSnmhMRERERERGpEa94ExERUYG8Fx2FVe0mmg6jWKdCvXH45+2aDkMl58+fh7u7u6bDICKicsbCm4iIiAqkU0kfunqGmg6jWNraOjA0rPhxAoC+vr6mQyAiIg3gVHMiIiIiIiIiNWLhTURERERERKRGLLyJiIiIiIiI1IiFNxEREREREZEasfAmIiIiBYbmNmjpNxuG5jaaDuWdY2Njg6CgINjYMLdERO8TmRBCaDoIoookLS0NpqamGLMtBXIDE02HQ0RExTixrA9OHYrVdBhERESF4hVvIiIiIiIiIjVi4U1ERERERESkRiy8iYiIiIiIiNSIhTcRERERERGRGrHwJiIiIiIiIlIjFt5EREREREREasTCm4iIiIiIiEiNWHgTERERERERqRELbyIiIiIiIiI10tF0AEQV1elVvtDR0dV0GEREVAxrC1NNh0BERFQkmRBCaDoIoookLS0NpqamSE1NhYmJiabDISIiIiKitxynmhMRERERERGpEQtvIiIiIiIiIjVi4U1ERERERESkRiy8iYiIiIiIiNSIhTcRERERERGRGrHwJiIiIiIiIlIjFt5EREREREREasTCm4iIiIiIiEiNWHgTERERERERqRELbyIiIiIiIiI1YuFNREREREREpEYsvImIiIiIiIjUiIU3ERERERERkRqx8CYiIiIiIiJSIxbeRERERERERGrEwpuIiIiIiIhIjXQ0HQBRRdW/b3/o6PCfCBER0fvKzNwMP279UdNhENE7gFUFUSEWzlkAIyMjTYdBREREGjJp2mRNh0BE7whONSciIiIiIiJSIxbeRERERERERGrEwpuIiIiIiIhIjVh4ExEREREREakRC28iIiIiIiIiNWLhTURERERERKRGLLyJiIiIiIiI1IiFNxEREREREZEasfAmIiIiogrlYVISVq1bhYdJSZoORaMSExMRHByMxMRETYdCRG+IhTcRERERVShJj5KwOmwNkh6x8A4JCWHhTfQOYOFNREREREREpEYsvImIiIiIiIjUiIU3ERERERERkRrpaDoAIiIiIqKCPM96jmeZzzR2/JzcHGRkZGjs+JmZmRo7NhGVLRbeRERERFQhDRo6SNMhwMjISNMhENE7gFPNiYiIiIiIiNSIV7yJiIiIqELavGEz6tdz0tjxp836HD/F/qSx458/fx7u7u4aOz4RlR0W3kRERERUIenJ9WCgb6Cx4+to68DQ0FBjx9fX19fYsYmobHGqOREREREREZEasfAmIiIiIiIiUiMW3kRERERERERqxMKbiIiIiIiISI1YeBMRERFRhWJpYYmxo8fA0sJS06FolI2NDYKCgmBjY6PpUIjoDXFVcyIiIiKqUKwsLTEucJymw9A4GxsbBAcHazoMIioDvOJNREREREREpEYsvImIiIiIiIjUiIU3ERERERERkRq9s4V3hw4d0KFDB+n93bt3IZPJEBERUe6xBAQEwN7evtyPSy+9/rtARERERERUnipM4X379m2MHj0atWvXhp6eHkxMTNCmTRusXLkSmZmZmg6vWP/++y+Cg4Nx/vx5TYdS7t7ncyciIiIiIipOhVjVfO/evRg4cCDkcjn8/f3h4uKC7OxsHDt2DFOnTsWff/6J8PBwTYdZpH///RchISGwt7dHkyZNFLatX78eeXl5mgmsHBR17kRERERERO87jRfed+7cga+vL2rWrIlDhw4pPKdw7NixuHXrFvbu3avBCN+crq6upkN4K2VkZMDQ0FDTYRAREREREb0RjU81X7x4MdLT0/Hdd98pFN35HB0dMXHiROl9Tk4O5s6dCwcHB8jlctjb22PGjBnIysoq1fGvXbuGAQMGwNzcHHp6emjWrBliY2OV+qWkpGDy5Mmwt7eHXC6Hra0t/P398ejRIxw+fBjNmzcHAAwdOhQymUzhfvJX7/F+8eIFzM3NMXToUKVjpKWlQU9PD1OmTJHasrKyEBQUBEdHR8jlctjZ2WHatGkqnW+HDh3g4uKCixcvon379jAwMICjoyN27NgBADhy5Ajc3Nygr6+PevXq4eDBg0pjJCQkYNiwYahatSrkcjmcnZ3x/fffS9uLO3cAOH36NLp16wZTU1MYGBigffv2OH78uMJxgoODIZPJcOXKFXz00UcwMzODu7u7tH3z5s1wdXWFvr4+zM3N4evri3/++Ucp3vDwcDg4OEBfXx8tWrTA0aNHi80TERERERGROmm88N69ezdq166N1q1bq9R/xIgRmD17Nj744AMsX74c7du3x4IFC+Dr61viY//5559o2bIlrl69ii+++AJff/01DA0N0bdvX+zcuVPql56ejrZt2yI0NBRdu3bFypUrERgYiGvXruH+/fuoX78+5syZAwAYNWoUNm3ahE2bNqFdu3ZKx9TV1UW/fv2wa9cuZGdnK2zbtWsXsrKypHPJy8uDp6cnli5dit69eyM0NBR9+/bF8uXL4ePjo9I5PnnyBL169YKbmxsWL14MuVwOX19fbNu2Db6+vujRowcWLlyIjIwMDBgwAE+fPpX2ffDgAVq2bImDBw9i3LhxWLlyJRwdHTF8+HCsWLECAIo990OHDqFdu3ZIS0tDUFAQ5s+fj5SUFHTq1AlnzpxRinfgwIF49uwZ5s+fj5EjRwIAvvrqK/j7+6NOnTpYtmwZJk2ahF9//RXt2rVDSkqKtO93332H0aNHw9raGosXL0abNm3g6elZYIH+qqysLKSlpSm8iIiIiIiIyopMCCE0dfC0tDSYmpqiT58+2LVrV7H9L1y4gCZNmmDEiBFYv3691D516lQsXboUhw4dQseOHQFAWsX68OHDAF6ual6rVi1s2LABAQEBAIAuXbrg4cOHOHv2LORyOQBACAF3d3ckJSXhxo0bAICgoCDMmTMHMTEx6Nevn0JMQgjIZDL89ttvaN68ucL4+QICAnD48GHcvXsXAPDLL7/Aw8MDu3fvRq9evaR+PXv2xLVr13D79m0AL6/yDhkyBEeOHFG4+hsWFobAwEAcP368yC8sOnTogCNHjmDLli3w8/MDAFy/fh1OTk7Q0tLCiRMn4ObmphDTq/GPGDECP//8My5duoQqVapI4/r5+SEuLg6JiYnQ19cv9NyFEKhXrx5q166NuLg4yGQyAEBmZiacnZ3h6OiIX375BcDLK94hISHw8/PDli1bpDHu3bsHBwcHzJkzBzNmzJDaL1++jKZNmyIkJAQzZszAixcvYGtri2rVquH06dOoVKkSgJf3148aNQrt27eXfhdel3/s19WtWx/a2tqF5peI1EtbWwvVC5gJRURUXszMzfDj1h81HQYRvQM0eo93/pVFY2Njlfr//PPPAIBPP/1Uof2zzz7D0qVLsXfvXqnwLk5ycjIOHTqEOXPm4OnTpwpXej08PBAUFISEhARUr14d0dHRaNy4sVLRDUAqJkuiU6dOsLCwwLZt26TC+8mTJzhw4IDCNPPt27ejfv36cHJywqNHjxT2B4D4+PhiZwoYGRkpzAaoV68eKleujOrVq0tFNwDpv//66y8AL4vm6OhoeHt7QwihcHwPDw9s3boV586dQ5s2bQo99vnz53Hz5k3MmjULjx8/VtjWuXNnbNq0CXl5edDS+v8TLwIDAxX6xcTEIC8vD97e3goxWFtbo06dOoiPj8eMGTPw22+/4eHDh5gzZ45UdAMvv/SYOnVqkTmaPn26wu9UWloa7Ozs0GXuScgNTIrcl4jU58SyPtj3i/KtP0RERERvG40W3iYmL4uaV4veoty7dw9aWlpwdHRUaLe2tkblypVx7949lY9969YtCCHw5Zdf4ssvvyywz8OHD1G9enXcvn0bXl5eKo9dHB0dHXh5eWHLli3IysqCXC5HTEwMXrx4oTCF/ObNm7h69SosLS0Lja84tra2Sl8OmJqaws7OTqkNePkFAAAkJSUhJSUF4eHhha4oX9zxb968CQAYMmRIoX1SU1NhZmYmva9Vq5bSGEII1KlTp8D98xeuy//Zv95PV1cXtWvXLjJOuVwuzXggIiIiIiIqaxovvKtVq4bLly+XaL/SXGV+Xf7jvaZMmQIPD48C+7xe4JclX19fhIWFIS4uDn379kVUVBScnJzQuHFjhRgbNmyIZcuWFTjG68VzQQqbKl1Ye/6dB/n5GTRoUKGFc6NGjYo8dv4YS5YsKfQxY0ZGRgrv9fX1lcaQyWSIi4srMObX9yciIiIiIqpoNP44sV69eiE8PBwnT55Eq1atiuxbs2ZN5OXl4ebNm6hfv77U/uDBA6SkpKBmzZoqHzf/Kqiuri66dOlSZF8HB4divxwo6ZcB7dq1g42NDbZt2wZ3d3ccOnQIM2fOVDruhQsX0Llz5zL5sqEkLC0tYWxsjNzc3GLzU1hsDg4OAF5+wVLcGIVxcHCAEAK1atVC3bp1C+2X/7O/efOmNBUfeLmK/J07dxS+0CAiIiIiIipPGl/VfNq0aTA0NMSIESPw4MEDpe23b9/GypUrAQA9evQAAGlF7Xz5V4R79uyp8nGtrKzQoUMHhIWFITExUWl7UlKS9N9eXl64cOGCwkrn+fKvEOc/b/rVVbaLoqWlhQEDBmD37t3YtGkTcnJylFYq9/b2RkJCgsJCcvkyMzORkZGh0rFKQ1tbG15eXoiOji7wS4dX81PYubu6usLBwQFLly5Fenp6kWMUpn///tDW1kZISAheXwdQCCHdO96sWTNYWlpi3bp1CqvFR0REqPwzISIiIiIiUgeNX/F2cHDAli1b4OPjg/r168Pf3x8uLi7Izs7GiRMnsH37dmml7MaNG2PIkCEIDw9HSkoK2rdvjzNnzmDjxo3o27evygur5Vu9ejXc3d3RsGFDjBw5ErVr18aDBw9w8uRJ3L9/HxcuXADwctX0HTt2YODAgRg2bBhcXV2RnJyM2NhYrFu3Do0bN4aDgwMqV66MdevWwdjYGIaGhnBzc1O6Z/lVPj4+CA0NRVBQEBo2bKhwFR8ABg8ejKioKAQGBiI+Ph5t2rRBbm4url27hqioKOzfvx/NmjUrWcJLYOHChYiPj4ebmxtGjhyJBg0aIDk5GefOncPBgweRnJwMAEWe+7fffovu3bvD2dkZQ4cORfXq1ZGQkID4+HiYmJhg9+7dRcbg4OCAefPmYfr06bh79y769u0LY2Nj3LlzBzt37sSoUaMwZcoU6OrqYt68eRg9ejQ6deoEHx8f3LlzBxs2bCj2Hm8iIiIiIiJ10njhDQCenp64ePEilixZgp9++glr166FXC5Ho0aN8PXXX0vPcwaAb7/9FrVr10ZERAR27twJa2trTJ8+HUFBQSU+boMGDfDbb78hJCQEERERePz4MaysrNC0aVPMnj1b6mdkZISjR48iKCgIO3fuxMaNG2FlZYXOnTvD1tYWwMsp6xs3bsT06dMRGBiInJwcbNiwocjCu3Xr1rCzs8M///xT4HO5tbS0sGvXLixfvhw//PADdu7cCQMDA9SuXRsTJ04scup1WahatSrOnDkjPUptzZo1qFKlCpydnbFo0SKpX1Hn3qFDB5w8eRJz587FqlWrkJ6eDmtra7i5uWH06NEqxfHFF1+gbt26WL58ufTYLzs7O3Tt2hWenp5Sv1GjRiE3NxdLlizB1KlT0bBhQ8TGxha6eB4REREREVF50OhzvIkqovzny4/ZlsLHiRFp0IllfXDqEB8nRkRERG8/jd/jTURERERERPQuY+FNREREREREpEYsvImIqFjpyYk4uSUY6cnKT4F42yUmJiI4OLjAJ1wQERERlQUW3kREVKyM5ESc+nEOMt7RwjskJISFNxEREakNC28iIiIiIiIiNWLhTURERERERKRGLLyJiIiIiIiI1EhH0wEQEdHbIyc7Ey+eZ5TLsXJzc5CRof5jZWZmqv0YRERE9H5j4U1ERCqL+rxtuR7PyMioXI9HREREpA6cak5ERERERESkRrziTUREKvNedBRWtZuUy7FOhXrj8M/b1X6c8+fPw93dXe3HISIiovcXC28iIlKZTiV96OoZlsuxtLV1YGio/mPp6+ur/RhERET0fuNUcyIiIiIiIiI1YuFNREREREREpEYsvImIiIiIiIjUiIU3ERERERERkRqx8CYiomIZmtugpd9sGJrbaDqUMmdjY4OgoCDY2Lx750ZEREQVg0wIITQdBFFFkpaWBlNTU4zZlgK5gYmmwyF6b51Y1genDsVqOgwiIiKiN8Yr3kRERERERERqxMKbiIiIiIiISI1YeBMRERERERGpEQtvIiIiIiIiIjVi4U1ERERERESkRiy8iYiIiIiIiNSIhTcRERERERGRGrHwJiIiIiIiIlIjHU0HQFRRnV7lCx0dXU2HQfTesrYw1XQIRERERGVCJoQQmg6CqCJJS0uDqakpUlNTYWJioulwiIiIiIjoLcep5kRERERERERqxMKbiIiIiIiISI1YeBMRERERERGpEQtvIiIiIiIiIjVi4U1ERERERESkRiy8iYiIiIiIiNSIhTcRERERERGRGrHwJiIiIiIiIlIjFt5EREREREREasTCm4iIiIiIiEiNWHgTERERERERqRELbyIiIiIiIiI1YuFNREREREREpEYsvImIiIiIiIjUiIU3ERERERERkRqx8CYiIiIiIiJSIx1NB0BUUfXv2x86OvwnQkRE7zYzczP8uPVHTYdBRPROY1VBVIiFcxbAyMhI02EQERGp1aRpkzUdAhHRO49TzYmIiIiIiIjUiIU3ERERERERkRqx8CYiIiIiIiJSIxbeRERERERERGrEwpuIiIiIiIhIjVh4ExEREREREakRC28iIiIiIiIiNWLhTURERERERKRGLLyJiIiIiIiI1IiFNxEREb0XHiYlYdW6VXiYlKTpUKiCS0xMRHBwMBITEzUdChG9I1h4ExER0Xsh6VESVoetQdIjFt5UtMTERISEhLDwJqIyw8KbiIiIiIiISI1YeBMRERERERGpEQtvIiIiIiIiIjXS0XQAREREROXpedZzPMt8pukwKoyc3BxkZGRoOowKJTMzU9MhENE7hoU3ERERvVcGDR2k6RAqHCMjI02HQET0TuNUcyIiIiIiIiI14hVvIiIieq9s3rAZ9es5aTqMCmParM/xU+xPmg6jQjl//jzc3d01HQYRvUNYeBMREdF7RU+uBwN9A02HUWHoaOvA0NBQ02FUKPr6+poOgYjeMZxqTkRERERERKRGLLyJiIiIiIiI1IiFNxEREREREZEasfAmIiIiIiIiUiMW3kRERPResLSwxNjRY2BpYanpUKiCs7GxQVBQEGxsbDQdChG9I7iqOREREb0XrCwtMS5wnKbDoLeAjY0NgoODNR0GEb1DeMWbiIiIiIiISI1YeBMRERERERGpUYUovDt06IAOHTpI7+/evQuZTIaIiIhyjyUgIAD29vblflxSn9d/v4iIiIiIiMpTqQrv27dvY/To0ahduzb09PRgYmKCNm3aYOXKlcjMzCzrGMvcv//+i+DgYJw/f17TobwTmE8iIiIiIqLClXhxtb1792LgwIGQy+Xw9/eHi4sLsrOzcezYMUydOhV//vknwsPD1RFrmfn3338REhICe3t7NGnSRGHb+vXrkZeXp5nA3lJF5ZOIiIiIiOh9V6LC+86dO/D19UXNmjVx6NAhhUcsjB07Frdu3cLevXvLPMjypKurq+kQ6P9kZGTA0NBQ02EQERERERG9kRJNNV+8eDHS09Px3XffFfhcQ0dHR0ycOFF6n5OTg7lz58LBwQFyuRz29vaYMWMGsrKyShXstWvXMGDAAJibm0NPTw/NmjVDbGysUr+UlBRMnjwZ9vb2kMvlsLW1hb+/Px49eoTDhw+jefPmAIChQ4dCJpMp3E/+6j3eL168gLm5OYYOHap0jLS0NOjp6WHKlClSW1ZWFoKCguDo6Ai5XA47OztMmzZNpfO1t7dHQECAUvvr9ycfPnwYMpkMUVFR+Oqrr2Braws9PT107twZt27dUtrXxcUFFy9eRPv27WFgYABHR0fs2LEDAHDkyBG4ublBX18f9erVw8GDB5WOn5CQgGHDhqFq1aqQy+VwdnbG999/rxBPUfkEgNOnT6Nbt24wNTWFgYEB2rdvj+PHjyscJzg4GDKZDFeuXMFHH30EMzMzuLu7S9s3b94MV1dX6Ovrw9zcHL6+vvjnn3+U4g0PD4eDgwP09fXRokULHD16tPCkExERERERlYMSFd67d+9G7dq10bp1a5X6jxgxArNnz8YHH3yA5cuXo3379liwYAF8fX1LHOiff/6Jli1b4urVq/jiiy/w9ddfw9DQEH379sXOnTulfunp6Wjbti1CQ0PRtWtXrFy5EoGBgbh27Rru37+P+vXrY86cOQCAUaNGYdOmTdi0aRPatWundExdXV3069cPu3btQnZ2tsK2Xbt2ISsrSzqXvLw8eHp6YunSpejduzdCQ0PRt29fLF++HD4+PiU+3+IsXLgQO3fuxJQpUzB9+nScOnUKH3/8sVK/J0+eoFevXnBzc8PixYshl8vh6+uLbdu2wdfXFz169MDChQuRkZGBAQMG4OnTp9K+Dx48QMuWLXHw4EGMGzcOK1euhKOjI4YPH44VK1YAQLH5PHToENq1a4e0tDQEBQVh/vz5SElJQadOnXDmzBmleAcOHIhnz55h/vz5GDlyJADgq6++gr+/P+rUqYNly5Zh0qRJ+PXXX9GuXTukpKRI+3733XcYPXo0rK2tsXjxYrRp0waenp4FFuivysrKQlpamsKLiIiIiIiorKg81TwtLQ0JCQno06ePSv0vXLiAjRs3YsSIEVi/fj0AYMyYMbCyssLSpUsRHx+Pjh07qhzoxIkTUaNGDZw9exZyuVwaz93dHZ9//jn69esHAFiyZAkuX76MmJgYqQ0AZs2aBSEEZDIZunfvjtmzZ6NVq1YYNGhQkcf18fHB999/j19++QW9evWS2rdt24batWujWbNmAIAtW7bg4MGDOHLkiMKVWhcXFwQGBuLEiRMqf2GhiufPn+P8+fOoVKkSAMDMzAwTJ07E5cuX4eLiIvX7999/sWXLFvj5+QEAPvzwQzg5OeGjjz7CiRMn4ObmBuBlAe3h4YHo6GjpyvvMmTORm5uLS5cuoUqVKgCAwMBA+Pn5ITg4GKNHj0bVqlULzacQAoGBgejYsSPi4uIgk8kAAKNHj4azszNmzZqFX375ReG8GjdujC1btkjv7927h6CgIMybNw8zZsyQ2vv374+mTZtizZo1mDFjBl68eIEZM2agSZMmiI+Pl/LSoEEDjBo1CnZ2doXmcsGCBQgJCVFq/3j4EGhraxfzk6D3iba2FqoXMNuHiOhtZmZupukQiIjeeSUqvAHA2NhYpf4///wzAODTTz9VaP/ss8+wdOlS7N27V+XCOzk5GYcOHcKcOXPw9OlThauyHh4eCAoKQkJCAqpXr47o6Gg0btxYoejOl1/4lUSnTp1gYWGBbdu2SYX3kydPcODAAYVp5tu3b0f9+vXh5OSER48eKewPAPHx8WVaeA8dOlQqLgGgbdu2AIC//vpLofA2MjJSmGFQr149VK5cGdWrV5eKbgDSf//1118AXhbN0dHR8Pb2hhBC4Zw8PDywdetWnDt3Dm3atCk0xvPnz+PmzZuYNWsWHj9+rLCtc+fO2LRpE/Ly8qCl9f8nXgQGBir0i4mJQV5eHry9vRVisLa2Rp06dRAfH48ZM2bgt99+w8OHDzFnzhyFvAQEBGDq1KmFxggA06dPV/g9TUtLg52dHbrMPQm5gUmR+9L75cSyPtj3i/LtLURERERERVG58DYxeVmAvFr0FuXevXvQ0tKCo6OjQru1tTUqV66Me/fuqRzkrVu3IITAl19+iS+//LLAPg8fPkT16tVx+/ZteHl5qTx2cXR0dODl5YUtW7YgKysLcrkcMTExePHihcIU8ps3b+Lq1auwtLQsNL6yVKNGDYX3ZmYvv61+8uSJQrutra3SFw6mpqZKV4BNTU0V9k9KSkJKSgrCw8MLXaW+uHO6efMmAGDIkCGF9klNTZViB4BatWopjSGEQJ06dQrcP38xvPzfp9f76erqonbt2kXGKZfLpVkUREREREREZa1EhXe1atVw+fLlEh2gNFeZX5f/eK8pU6bAw8OjwD6vF/hlydfXF2FhYYiLi0Pfvn0RFRUFJycnNG7cWCHGhg0bYtmyZQWOUdRUZ6DwPOXm5hY43bmwKdBCCJX6Fbd/fs4HDRpUaOHcqFGjAtvz5Y+xZMmSQh8zZmRkpPBeX19faQyZTIa4uLgCY359fyIiIiIiooqmRI8T69WrF8LDw3Hy5Em0atWqyL41a9ZEXl4ebt68ifr160vtDx48QEpKCmrWrKnycfOvWOrq6qJLly5F9nVwcCj2y4GSfhnQrl072NjYYNu2bXB3d8ehQ4cwc+ZMpeNeuHABnTt3LtWXDWZmZgoLheW7d+9esVds1cHS0hLGxsbIzc0tNueFna+DgwOAl1/aFDdGYRwcHCCEQK1atVC3bt1C++X/Pt28eVOa3g+8XJn+zp07Cl+SEBERERERlacSrWo+bdo0GBoaYsSIEXjw4IHS9tu3b2PlypUAgB49egCAtPp1vvwrwj179lT5uFZWVujQoQPCwsKQmJiotD0pKUn6by8vL1y4cEFhpfN8+Vdz858NXVChWxAtLS0MGDAAu3fvxqZNm5CTk6O0Urm3tzcSEhKkheRelZmZiYyMjCKP4eDggFOnTimsnr5nz55iV+RWF21tbXh5eSE6OrrALzJezXlh+XR1dYWDgwOWLl2K9PT0IscoTP/+/aGtrY2QkBClq/lCCOne8WbNmsHS0hLr1q1TyGFERITKP2ciIiIiIiJ1KNEVbwcHB2zZsgU+Pj6oX78+/P394eLiguzsbJw4cQLbt2+XVsRu3LgxhgwZgvDwcKSkpKB9+/Y4c+YMNm7ciL59+5ZoRXMAWL16Ndzd3dGwYUOMHDkStWvXxoMHD3Dy5Encv38fFy5cAABMnToVO3bswMCBAzFs2DC4uroiOTkZsbGxWLduHRo3bgwHBwdUrlwZ69atg7GxMQwNDeHm5qZ0f/GrfHx8EBoaiqCgIDRs2FDhKj4ADB48GFFRUQgMDER8fDzatGmD3NxcXLt2DVFRUdi/f7+0AnpBRowYgR07dqBbt27w9vbG7du3sXnzZumqsSYsXLgQ8fHxcHNzw8iRI9GgQQMkJyfj3LlzOHjwIJKTkwGgyHx+++236N69O5ydnTF06FBUr14dCQkJiI+Ph4mJCXbv3l1kDA4ODpg3bx6mT5+Ou3fvom/fvjA2NsadO3ewc+dOjBo1ClOmTIGuri7mzZuH0aNHo1OnTvDx8cGdO3ewYcMGjcwYICIiIiIiyleiwhsAPD09cfHiRSxZsgQ//fQT1q5dC7lcjkaNGuHrr7+Wnr0MAN9++y1q166NiIgI7Ny5E9bW1pg+fTqCgoJKHGiDBg3w22+/ISQkBBEREXj8+DGsrKzQtGlTzJ49W+pnZGSEo0ePIigoCDt37sTGjRthZWWFzp07w9bWFsDLKesbN27E9OnTERgYiJycHGzYsKHIwrt169aws7PDP//8U+BzubW0tLBr1y4sX74cP/zwA3bu3AkDAwPUrl0bEydOLHKaNPBypfCvv/5aek51s2bNsGfPHnz22WclzlVZqVq1Ks6cOYM5c+YgJiYGa9asQZUqVeDs7IxFixZJ/YrKZ4cOHXDy5EnMnTsXq1atQnp6OqytreHm5obRo0erFMcXX3yBunXrYvny5dJjv+zs7NC1a1d4enpK/UaNGoXc3FwsWbIEU6dORcOGDREbG1vognxERERERETlQSZen79L9J5LS0uDqakpxmxL4ePESMGJZX1w6hAfJ0ZEREREJVOie7yJiIiIiIiIqGRYeBMRERERERGpEQtvInprpScn4uSWYKQnKz/t4G2VmJiI4ODgAp/gQERERERvJxbeRPTWykhOxKkf5yDjHSu8Q0JCWHgTERERvUNYeBMRERERERGpEQtvIiIiIiIiIjVi4U1ERERERESkRjqaDoCI6E3lZGfixfMMtR8nNzcHGRnqPU5mZqZaxyciIiKi8sfCm4jeelGfty23YxkZGZXbsYiIiIjo3cCp5kRERERERERqxCveRPTW8150FFa1m6j9OKdCvXH45+1qPcb58+fh7u6u1mMQERERUfli4U1Ebz2dSvrQ1TNU+3G0tXVgaKje4+jr66t1fCIiIiIqf5xqTkRERERERKRGLLyJiIiIiIiI1IiFNxEREREREZEasfAmIiIiIiIiUiMW3kT01jI0t0FLv9kwNLfRdChlxsbGBkFBQbCxeXfOiYiIiOh9JxNCCE0HQVSRpKWlwdTUFGO2pUBuYKLpcKgCObGsD04ditV0GERERET0luEVbyIiIiIiIiI1YuFNREREREREpEYsvImIiIiIiIjUiIU3ERERERERkRqx8CYiIiIiIiJSIxbeRERERERERGrEwpuIiIiIiIhIjVh4ExEREREREamRjqYDIKqoTq/yhY6OrqbDoArE2sJU0yEQERER0VtIJoQQmg6CqCJJS0uDqakpUlNTYWJioulwiIiIiIjoLcep5kRERERERERqxMKbiIiIiIiISI1YeBMRERERERGpEQtvIiIiIiIiIjVi4U1ERERERESkRiy8iYiIiIiIiNSIhTcRERERERGRGrHwJiIiIiIiIlIjFt5EREREREREasTCm4iIiIiIiEiNdDQdAFFFI4QAAKSlpWk4EiIiIiLSFGNjY8hkMk2HQe8IFt5Er3n8+DEAwM7OTsOREBEREZGmpKamwsTERNNh0DuChTfRa8zNzQEAf//9N0xNTTUczdstLS0NdnZ2+Oeff/h/XGWA+Sw7zGXZYj7LFvNZdpjLsvW+5dPY2FjTIdA7hIU30Wu0tF4ufWBqavpe/J9KeTAxMWEuyxDzWXaYy7LFfJYt5rPsMJdli/kkKjkurkZERERERESkRiy8iYiIiIiIiNSIhTfRa+RyOYKCgiCXyzUdyluPuSxbzGfZYS7LFvNZtpjPssNcli3mk6j0ZCL/2UlEREREREREVOZ4xZuIiIiIiIhIjVh4ExEREREREakRC28iIiIiIiIiNWLhTURERERERKRGLLyJ/k9WVhY+//xzVKtWDfr6+nBzc8OBAwc0HZZapKenIygoCN26dYO5uTlkMhkiIiIK7Hv16lV069YNRkZGMDc3x+DBg5GUlKTysU6cOAF3d3cYGBjA2toaEyZMQHp6ulK/kuRf1THLw9mzZzFu3Dg4OzvD0NAQNWrUgLe3N27cuKHUl7ks3p9//omBAweidu3aMDAwgIWFBdq1a4fdu3cr9WU+S+6rr76CTCaDi4uL0rY3jT02NhYffPAB9PT0UKNGDQQFBSEnJ0epX0pKCkaNGgVLS0sYGhqiY8eOOHfu3BuNWR4OHz4MmUxW4OvUqVMKfZlL1Zw7dw6enp4wNzeHgYEBXFxc8M033yj0YS6LFxAQUOjvpkwmQ0JCgtSX+STSIEFEQgghfH19hY6OjpgyZYoICwsTrVq1Ejo6OuLo0aOaDq3M3blzRwAQNWrUEB06dBAAxIYNG5T6/fPPP8LCwkI4ODiIlStXiq+++kqYmZmJxo0bi6ysrGKP88cffwg9PT3RtGlTsXbtWjFz5kwhl8tFt27dlPqqmv+SjFkevLy8hLW1tRg/frxYv369mDt3rqhataowNDQUly5dkvoxl6rZu3ev8PDwEMHBwSI8PFysWLFCtG3bVgAQYWFhUj/ms+T++ecfYWBgIAwNDYWzs7PCtjeN/eeffxYymUx07NhRhIeHi/HjxwstLS0RGBio0C83N1e0bt1aGBoaiuDgYLFq1SrRoEEDYWxsLG7cuFGqMctLfHy8ACAmTJggNm3apPBKSkqS+jGXqtm/f7+oVKmScHNzE8uWLRPh4eHi888/F1OnTpX6MJeqOXHihNLv5A8//CAMDAxEgwYNpH7MJ5FmsfAmEkKcPn1aABBLliyR2jIzM4WDg4No1aqVBiNTj+fPn4vExEQhhBBnz54ttPD+5JNPhL6+vrh3757UduDAAaUiqDDdu3cXNjY2IjU1VWpbv369ACD2798vtZUk/6qOWV6OHz+uVOjduHFDyOVy8fHHH0ttzGXp5eTkiMaNG4t69epJbcxnyfn4+IhOnTqJ9u3bKxXebxp7gwYNROPGjcWLFy+ktpkzZwqZTCauXr0qtW3btk0AENu3b5faHj58KCpXriz8/PxKNWZ5yS+8X429IMxl8VJTU0XVqlVFv379RG5ubqH9mMvSO3r0qAAgvvrqK6mN+STSLBbeREKIqVOnCm1tbYX/MxJCiPnz5wsA4u+//9ZQZOpXVOFtZWUlBg4cqNRet25d0blz5yLHTU1NFTo6OgpXL4QQIisrSxgZGYnhw4dLbarmvyRjatoHH3wgPvjgA+k9c/lmevXqJapWrSq9Zz5L5siRI0JbW1tcvHhRqfB+09j//PNPAUCsXr1aoT0hIUEAEHPnzpXaBg4cKKpWrapUbI0aNUoYGBiI58+fl3jM8vJq4Z2WlqZQKORjLlWzdu1aAUBcuXJFCCFEenq60nkwl2/mk08+ETKZTNy5c0cIwXwSVQS8x5sIwB9//IG6devCxMREob1FixYAgPPnz2sgKs1KSEjAw4cP0axZM6VtLVq0wB9//FHk/pcuXUJOTo7S/pUqVUKTJk0U9lc1/yUZU5OEEHjw4AEsLCwAMJelkZGRgUePHuH27dtYvnw54uLi0LlzZwDMZ0nl5uZi/PjxGDFiBBo2bKi0/U1jz9/++v7VqlWDra2tUj4/+OADaGkp/vnRokULPHv2TFoboSRjlrehQ4fCxMQEenp66NixI3777TdpG3OpmoMHD8LExAQJCQmoV68ejIyMYGJigk8++QTPnz8HwFy+iRcvXiAqKgqtW7eGvb09AOaTqCJg4U0EIDExETY2Nkrt+W3//vtveYekcYmJiQBQaF6Sk5ORlZVV6v1fzamq+S/JmJoUGRmJhIQE+Pj4AGAuS+Ozzz6DpaUlHB0dMWXKFPTr1w+rVq0CwHyW1Lp163Dv3j3MnTu3wO1vGvv7ks9KlSrBy8sLK1euxE8//YR58+bh0qVLaNu2rVQgMJequXnzJnJyctCnTx94eHggOjoaw4YNw7p16zB06FAAzOWb2L9/Px4/foyPP/5YamM+iTRPR9MBEFUEmZmZkMvlSu16enrS9vdN/jkXl5eCtquy/6s5VTX/JRlTU65du4axY8eiVatWGDJkCADmsjQmTZqEAQMG4N9//0VUVBRyc3ORnZ0NgPksicePH2P27Nn48ssvYWlpWWCfN429uP3T0tIU+pZFPl8ds7y0bt0arVu3lt57enpiwIABaNSoEaZPn459+/YxlypKT0/Hs2fPEBgYKK1i3r9/f2RnZyMsLAxz5sxhLt/Ali1boKurC29vb6mN+STSPF7xJgKgr69f4BWy/Clv+vr65R2SxuWfc2nzUtz+r+6rav5LMqYm/Pfff+jZsydMTU2xY8cOaGtrA2AuS8PJyQldunSBv78/9uzZg/T0dPTu3RtCCOazBGbNmgVzc3OMHz++0D5vGvv7lM/XOTo6ok+fPoiPj0dubi5zqaL8Y/r5+Sm0f/TRRwCAkydPMpellJ6ejp9++gkeHh6oUqWK1M58EmkeC28ivJzSlD/l6VX5bdWqVSvvkDQuf+pXYXkxNzcv9IqiKvu/mlNV81+SMctbamoqunfvjpSUFOzbt0/p/ADm8k0MGDAAZ8+exY0bN5hPFd28eRPh4eGYMGEC/v33X9y9exd3797F8+fP8eLFC9y9exfJyclvHPv7ks/C2NnZITs7GxkZGcylivKPWbVqVYV2KysrAMCTJ0+Yy1LatWsXnj17pjDNHHjz2N/XfBKVJRbeRACaNGmCGzduKE1rOn36tLT9fVO9enVYWloqLByU78yZM8XmxMXFBTo6Okr7Z2dn4/z58wr7q5r/koxZnp4/f47evXvjxo0b2LNnDxo0aKCwnbl8c/lTElNTU5lPFSUkJCAvLw8TJkxArVq1pNfp06dx48YN1KpVC3PmzHnj2PO3v77/v//+i/v37yvl89y5c8jLy1Poe/r0aRgYGKBu3bolHlPT/vrrL+jp6cHIyIi5VJGrqyuAl7+jr8cEAJaWlsxlKUVGRsLIyAienp4K7cwnUQWg6WXViSqCU6dOKT2r9/nz58LR0VG4ublpMDL1K+pxYoGBgUJfX1/hcWoHDx4UAMTatWuLHbtbt27CxsZGpKWlSW3ffvutACDi4uKktpLkX9Uxy0tOTo7w9PQUOjo6Yu/evYX2Yy5V8+DBA6W27Oxs8cEHHwh9fX3x9OlTIQTzqYqkpCSxc+dOpZezs7OoUaOG2Llzp7h48WKZxO7k5CQaN24scnJypLZZs2YJmUwmPTJKCCG2bt2q9HzfpKQkUblyZeHj41OqMcvLw4cPldrOnz8vdHV1haenp9TGXBbv3LlzAoD46KOPFNr9/PyEjo6OSEhIEEIwlyX18OFDoaOjIwYPHlzgduaTSLNYeBP9n4EDB0rPuAwLCxOtW7cWOjo64siRI5oOTS1CQ0PF3LlzxSeffCIAiP79+4u5c+eKuXPnipSUFCGEEH///beoUqWKcHBwEN98842YP3++MDMzEw0bNpSew5mvZs2aombNmgptv//+u5DL5aJp06Zi7dq1YubMmUJPT0907dpVKR5V81+SMcvDxIkTBQDRu3dvsWnTJqVXPuZSNX379hWdOnUSwcHBYv369WLu3LnCyclJABBff/211I/5LL3Xn+MtRMliByDat2+v0LZ7924hk8lEp06dRHh4uJgwYYLQ0tISI0eOVOiXk5MjWrZsKYyMjERISIhYvXq1cHZ2FsbGxuLatWulGrO8dOzYUfTo0UPMmzdPhIeHi0mTJgkDAwNhamqqUCAwl6oZNmyYACC8vb3F6tWrxcCBAwUAMX36dKkPc1kyoaGhAoDYt29fgduZTyLNYuFN9H8yMzPFlClThLW1tZDL5aJ58+aF/p/Xu6BmzZoCQIGvO3fuSP0uX74sunbtKgwMDETlypXFxx9/LP777z+l8SwsLETLli2V2o8ePSpat24t9PT0hKWlpRg7dqzCt+35SpJ/VccsD+3bty80j69PKmIui/fjjz+KLl26iKpVqwodHR1hZmYmunTpIn766Selvsxn6RRUeAuhWuxPnz4VAISvr6/S/jt37hRNmjQRcrlc2NrailmzZons7GylfsnJyWL48OGiSpUqwsDAQLRv316cPXu2wFhVHbM8rFy5UrRo0UKYm5sLHR0dYWNjIwYNGiRu3ryp1Je5LF52drYIDg4WNWvWFLq6usLR0VEsX75cqR9zqbqWLVsKKysrhavHr2M+iTRHJoQQZT19nYjeL1euXIGzszP27NmDnj17ajqctxpzWbaYz7L1888/o1evXrhw4QIaNmyo6XDeasxl2WEuyxbzSaQeXFyNiN5YfHw8WrVqxcKmDDCXZYv5LFvx8fHw9fXlH+NlgLksO8xl2WI+idSDV7yJiIiIiIiI1IhXvImIiIiIiIjUiIU3ERERERERkRqx8CYiIiIiIiJSIxbeRERERERERGrEwpuIiIiIiIhIjVh4ExEREREREakRC28iIiIiIiIiNWLhTURE9J4LCAiAkZGRSn1lMhmCg4PVG1AFERwcDJlMpukwiIjoHcDCm4iISm3NmjWQyWRwc3PTdChUjGfPniE4OBiHDx/WdCgac+XKFQQHB+Pu3bsaOf7hw4chk8mwY8eOArePGzeuwEI/Ly8PP/zwAz788ENYWFhAV1cXVlZW6Nq1K8LDw5GVlSX1PXDgAGQyGUJCQpTGuXPnDgwMDDBgwICyOykiIlIJC28iIiq1yMhI2Nvb48yZM7h165amw6EiPHv2DCEhIe994R0SEqJy4T1r1ixkZmaqN6hiZGZmokePHhgyZAiePXuGKVOmIDw8HJ9//jn09PQwZswYjBkzRur/4Ycf4qOPPsKCBQtw48YNhbHGjBkDXV1dfPPNN+V9GkRE7z0W3kREVCp37tzBiRMnsGzZMlhaWiIyMrLcY8jLy8Pz58/L/bj0ftDR0YGenp5GY5g8eTL279+PFStW4Pjx4/jiiy8wdOhQfPbZZ4iNjcXVq1fh6uqqsM/y5cthYGCAwMBAqW3r1q3Yt28f5s2bh2rVqpX3aRARvfdYeBMRUalERkbCzMwMPXv2xIABAxQK7xcvXsDc3BxDhw5V2i8tLQ16enqYMmWK1JaVlYWgoCA4OjpCLpfDzs4O06ZNU5hCC7y8v3jcuHGIjIyEs7Mz5HI59u3bBwBYunQpWrdujSpVqkBfXx+urq4FTunNzMzEhAkTYGFhAWNjY3h6eiIhIaHAe5cTEhIwbNgwVK1aFXK5HM7Ozvj+++9Vyk9+rNu3b0eDBg2gr6+PVq1a4dKlSwCAsLAwODo6Qk9PDx06dCjwKuz27dvh6uoKfX19WFhYYNCgQUhISFDok39/dkJCAvr27QsjIyNYWlpiypQpyM3NBQDcvXsXlpaWAICQkBDIZLJCz7ewMQoSHx8PmUyGnTt3Km3bsmULZDIZTp48WWSeUlJSMHnyZNjb20Mul8PW1hb+/v549OiR1Ofhw4cYPnw4qlatCj09PTRu3BgbN25UGmvr1q1wdXWFsbExTExM0LBhQ6xcuRIAEBERgYEDBwIAOnbsKOWgqBkABd3jnf9z3bVrF1xcXKTfi/zfw7L0zz//4Ntvv0W3bt0wceLEAvvUqVNH4Yo3AFhZWWHRokWIj4/Hxo0bpRw3b94cY8eOLfM4iYioeDqaDoCIiN5OkZGR6N+/PypVqgQ/Pz+sXbsWZ8+eRfPmzaGrq4t+/fohJiYGYWFhqFSpkrTfrl27kJWVBV9fXwAvr1p7enri2LFjGDVqFOrXr49Lly5h+fLluHHjBnbt2qVw3EOHDiEqKgrjxo2DhYUF7O3tAQArV66Ep6cnPv74Y2RnZ2Pr1q0YOHAg9uzZg549e0r7BwQEICoqCoMHD0bLli1x5MgRhe35Hjx4gJYtW0qFlqWlJeLi4jB8+HCkpaVh0qRJxebo6NGjiI2NlYqdBQsWoFevXpg2bRrWrFmDMWPG4MmTJ1i8eDGGDRuGQ4cOSftGRERg6NChaN68ORYsWIAHDx5g5cqVOH78OP744w9UrlxZ6pubmwsPDw+4ublh6dKlOHjwIL7++ms4ODjgk08+gaWlJdauXYtPPvkE/fr1Q//+/QEAjRo1UnmMgnTo0AF2dnaIjIxEv379FLZFRkbCwcEBrVq1KjQ/6enpaNu2La5evYphw4bhgw8+wKNHjxAbG4v79+/DwsICmZmZ6NChA27duoVx48ahVq1a2L59OwICApCSkiIVpAcOHICfnx86d+6MRYsWAQCuXr2K48ePY+LEiWjXrh0mTJiAb775BjNmzED9+vUBQPrfkjh27BhiYmIwZswYGBsb45tvvoGXlxf+/vtvVKlSpcTjFSYuLg65ubkYNGhQifcdMWIENm7ciClTpmD//v1ISkrCzz//DC0tXnMhItIIQUREVEK//fabACAOHDgghBAiLy9P2NraiokTJ0p99u/fLwCI3bt3K+zbo0cPUbt2ben9pk2bhJaWljh69KhCv3Xr1gkA4vjx41IbAKGlpSX+/PNPpZiePXum8D47O1u4uLiITp06SW2///67ACAmTZqk0DcgIEAAEEFBQVLb8OHDhY2NjXj06JFCX19fX2Fqaqp0vNcBEHK5XNy5c0dqCwsLEwCEtbW1SEtLk9qnT58uAEh9s7OzhZWVlXBxcRGZmZlSvz179ggAYvbs2VLbkCFDBAAxZ84cheM3bdpUuLq6Su+TkpKUzrGkY+Sf16tjTJ8+XcjlcpGSkiK1PXz4UOjo6BR4rFfNnj1bABAxMTFK2/Ly8oQQQqxYsUIAEJs3b5a2ZWdni1atWgkjIyMpjxMnThQmJiYiJyen0ONt375dABDx8fFFxpUvKChIvP6nEgBRqVIlcevWLantwoULAoAIDQ0tcrz4+HgBQGzfvr3A7WPHjlU43uTJkwUAcf78eYV+WVlZIikpSXq9/jua7/Lly0JXV7fA33kiIipf/NqTiIhKLDIyElWrVkXHjh0BvJx+6+Pjg61bt0pTkzt16gQLCwts27ZN2u/Jkyc4cOAAfHx8pLbt27ejfv36cHJywqNHj6RXp06dALyczvyq9u3bo0GDBkox6evrKxwnNTUVbdu2xblz56T2/OnAr0/NHT9+vMJ7IQSio6PRu3dvCCEU4vLw8EBqaqrCuIXp3LmzdEUegLT6u5eXF4yNjZXa//rrLwDAb7/9hocPH2LMmDEK9xj37NkTTk5O2Lt3r9KxXr2fFwDatm0rjaeq0ozh7++PrKwshWn927ZtQ05OTrFXaqOjo9G4cWOlq+UApCneP//8M6ytreHn5ydt09XVxYQJE5Ceno4jR44AACpXroyMjAwcOHCg6JMsA126dIGDg4P0vlGjRjAxMSlxvouTlpYGAEqPevv5559haWkpvWrWrFng/iYmJtJsk65du5ZpbEREVDIsvImIqERyc3OxdetWdOzYEXfu3MGtW7dw69YtuLm54cGDB/j1118BvFyYysvLCz/99JN0r3ZMTAxevHihUHjfvHkTf/75p0IhYWlpibp16wJ4eX/vq2rVqlVgXHv27EHLli2hp6cHc3NzaXp1amqq1OfevXvQ0tJSGsPR0VHhfVJSElJSUhAeHq4UV/5966/HVZAaNWoovDc1NQUA2NnZFdj+5MkTKU4AqFevntKYTk5O0vZ8enp60j3c+czMzKTxVFHaMZycnNC8eXOFe/wjIyPRsmVLpby+7vbt23BxcSmyz71791CnTh2lKdL5U8TzczFmzBjUrVsX3bt3h62tLYYNG6aW+64B5Z8rUPJ8qyL/y5n09HSF9jZt2uDAgQM4cOBAkQX1uHHjoKWlhZo1a+Kzzz7DixcvyjQ+IiJSHe/xJiKiEjl06BASExOxdetWbN26VWl7ZGSkVAz4+voiLCwMcXFx6Nu3L6KiouDk5ITGjRtL/fPy8tCwYUMsW7aswOO9XqS+emU739GjR+Hp6Yl27dphzZo1sLGxga6uLjZs2IAtW7aU+Bzz8vIAAIMGDcKQIUMK7PPq/dGF0dbWLlG7EELFCFUbr7zG8Pf3x8SJE3H//n1kZWXh1KlTWLVq1RvHVBJWVlY4f/489u/fj7i4OMTFxWHDhg3w9/cvcCG2N1Han1/+7IXCHlH27NkzhRkOTk5OAIDLly8r/JuxtLREly5dAACbN28ucKyYmBjExsZixYoVqFOnDnr27IklS5ZgxowZRcZIRETqwcKbiIhKJDIyElZWVli9erXStpiYGOzcuRPr1q2Dvr4+2rVrBxsbG2zbtg3u7u44dOgQZs6cqbCPg4MDLly4gM6dOyutIK2q6Oho6OnpYf/+/ZDL5VL7hg0bFPrVrFkTeXl5uHPnDurUqSO1v/4McktLSxgbGyM3N1cqcMpT/tTh69evS1Pu812/fr3QqcVFKW1uVeHr64tPP/0UP/74IzIzM6Grq6swq6EwDg4OuHz5cpF9atasiYsXLyIvL0/hqve1a9ek7fkqVaqE3r17o3fv3sjLy8OYMWMQFhaGL7/8Eo6OjmrNgSpe/bkW5PWfbffu3aGtrY3IyEh8/PHHKh/n6dOnmDBhAj744AOMGzcO2tra8PLywrx58+Dn51forBEiIlIfTjUnIiKVZWZmIiYmBr169cKAAQOUXuPGjcPTp08RGxsLANDS0sKAAQOwe/dubNq0CTk5OUoFmbe3NxISErB+/foCj5eRkVFsXNra2pDJZAqPvrp7967SiugeHh4AgDVr1ii0h4aGKo3n5eWF6OjoAgvDpKSkYmN6E82aNYOVlRXWrVun8Ei1uLg4XL16tcBV2ItjYGAA4OXju8qahYUFunfvjs2bNyMyMhLdunWDhYVFsft5eXnhwoULBT6OLP/qcY8ePfDff/8prBWQk5OD0NBQGBkZoX379gCAx48fK+yvpaUlzUrIz6GhoSEA9eRAFTY2NmjSpAk2b96sFMPvv/+OU6dOoXv37lJbjRo1MGzYMMTFxRU6g6Cgq+yzZs1CYmIiwsLCpKvzK1euhLa2NsaNG1d2J0RERCrjFW8iIlJZbGwsnj59Ck9PzwK3t2zZEpaWloiMjJQKbB8fH4SGhiIoKAgNGzZUenzT4MGDERUVhcDAQMTHx6NNmzbIzc3FtWvXEBUVhf3796NZs2ZFxtWzZ08sW7YM3bp1w0cffYSHDx9i9erVcHR0xMWLF6V+rq6u8PLywooVK/D48WPpcWI3btwAoHhVeOHChYiPj4ebmxtGjhyJBg0aIDk5GefOncPBgweRnJxcqhyqQldXF4sWLcLQoUPRvn17+Pn5SY8Ts7e3x+TJk0s8pr6+Pho0aIBt27ahbt26MDc3h4uLS7H3WKvK398fAwYMAADMnTtXpX2mTp2KHTt2YODAgRg2bBhcXV2RnJyM2NhYrFu3Do0bN8aoUaMQFhaGgIAA/P7777C3t8eOHTtw/PhxrFixQroPesSIEUhOTkanTp1ga2uLe/fuITQ0FE2aNJF+55o0aQJtbW0sWrQIqampkMvl6NSpE6ysrMokB6pYtmwZPDw80KRJEwQEBKBatWq4evUqwsPDYWNjg+nTpyv0X7FiBe7cuYPx48dj69at6N27N6ysrPDo0SMcP34cu3fvVlgL4Pfff8fq1asxduxYhX831atXx5w5c/Dpp58iOjoaXl5e5XbOREQEPk6MiIhU17t3b6GnpycyMjIK7RMQ8P/au3uXRqIojMNndTIpFCQhiORLEARBhMT0ggasFOxFR0G0iP+BVdDCj8ZSlDAWaQQLe3vRQhALwcpGkCAWohZK8N1q4w5GXJcNLvp7yuHm5tzJpDgh975TCoVCtYij5+dnpVIpmZmWlpbqvubp6UkrKyvq7e1VOBxWJBJRLpdTsVjU7e1tbZyZqVAo1J2jVCqpu7tb4XBYPT098n2/bhzUw8ODCoWCotGoWltbNTY2pvPzc5mZlpeXA2MrlYoKhYJSqZRCoZA6OjqUz+e1ubn57r2qV+vFxYXMTGtra4Hrb8VM7ezsKJvNKhwOKxqNanx8XJeXl4ExnueppaXl1fvXW/vBwYFyuZxc1w3Egn1kDnsjkuzx8VGRSERtbW2BCLT33NzcaH5+XolEQq7rKplMyvO8QERWpVLR9PS0YrGYXNdVX1+ffN8PzLO7u6vh4WG1t7fLdV2l02nNzc3p6uoqMG5ra0tdXV1qbm5+N1rsrfXXewY7Ozvled4frfnw8FAjIyOKRCJyHEeJREIzMzOvPttfqtWqfN/X0NCQotGoHMdRLBZTPp/XxsZG7X5Xq1X19/crHo8Hvje/z5PJZJRMJnV3d/dHtQIA/o0f0l+e5AIAwBdxcnJi2WzWyuXyh/bS4kW1WrV4PG6jo6NWKpU+uxwAAP4r7PEGAHwr9U6UXl9ft6amJhsYGPiEir6Gvb09u76+tsnJyc8uBQCA/w57vAEA38rq6qodHx/b4OCgOY5Ti56anZ19FV2G9x0dHdnp6aktLi5aNputHXYGAABe8FdzAMC3sr+/b8Vi0c7Ozuz+/t7S6bRNTEzYwsKCOQ6/R3/U1NSUlctly2Qytr29/c8OawMA4Cuh8QYAAAAAoIHY4w0AAAAAQAPReAMAAAAA0EA03gAAAAAANBCNNwAAAAAADUTjDQAAAABAA9F4AwAAAADQQDTeAAAAAAA0EI03AAAAAAAN9BO0W4HwatHk1wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define connection type mapping\n",
        "connection_type_mapping = {\n",
        "    2: 'Individual metered',\n",
        "    5: 'Collective metered',\n",
        "    6: 'Collective unmetered'\n",
        "}\n",
        "\n",
        "# Filter the DataFrame for the specified connection types\n",
        "filtered_df = expense_df[expense_df['Connection type'].isin(connection_type_mapping.keys())].copy()\n",
        "\n",
        "# Replace 999 values with NaN in the expense columns\n",
        "expense_columns = ['Electricity', 'Average monthly charcoal expense']\n",
        "filtered_df[expense_columns] = filtered_df[expense_columns].replace(999, np.nan)\n",
        "\n",
        "# Map connection types to their labels\n",
        "filtered_df['Connection type'] = filtered_df['Connection type'].map(connection_type_mapping)\n",
        "\n",
        "# Calculate mean expenses and SEM grouped by connection type\n",
        "mean_expenses = filtered_df.groupby('Connection type')[expense_columns].mean()\n",
        "sem_expenses = filtered_df.groupby('Connection type')[expense_columns].sem()\n",
        "\n",
        "# Define connection order and reverse it\n",
        "connection_order = ['Individual metered', 'Collective metered', 'Collective unmetered'][::-1]\n",
        "\n",
        "# Reindex expenses to match reversed order\n",
        "mean_expenses = mean_expenses.reindex(connection_order)\n",
        "sem_expenses = sem_expenses.reindex(connection_order)\n",
        "\n",
        "# Define expense categories and labels\n",
        "expense_order = ['Electricity', 'Average monthly charcoal expense']\n",
        "expense_labels = ['Electricity', 'Charcoal']\n",
        "colors = ['#71B3FE', '#D7CFD6']\n",
        "\n",
        "# Plotting\n",
        "fig, ax = plt.subplots(figsize=(10, 4))\n",
        "\n",
        "# Set the positions and width for the bars\n",
        "bar_width = 0.4\n",
        "r = np.arange(len(connection_order))  # Positions for bars\n",
        "\n",
        "# Create bars for each expense category with error bars\n",
        "for i, (expense, label, color) in enumerate(zip(expense_order, expense_labels, colors)):\n",
        "    ax.barh(r + i * bar_width, mean_expenses[expense], xerr=sem_expenses[expense],\n",
        "            height=bar_width, label=label, color=color, edgecolor='black', linewidth=0.5, capsize=5)\n",
        "\n",
        "# Set y-ticks in reversed order with consistent font size\n",
        "ax.set_yticks(r + bar_width * (len(expense_order) - 1) / 2)\n",
        "ax.set_yticklabels(connection_order, fontsize=12)  # **Ensuring same font size for y-axis labels**\n",
        "\n",
        "# Set x-axis label\n",
        "ax.set_xlabel('Average monthly cost in UGX', fontsize=12)\n",
        "\n",
        "# Set x-tick label font size for bottom axis\n",
        "ax.tick_params(axis='x', labelsize=12)  # **Ensuring same font size for x-tick labels**\n",
        "\n",
        "# Add legend with proper formatting\n",
        "legend = ax.legend(title='Fuel source', bbox_to_anchor=(1, 1.1), loc='upper left', frameon=False)\n",
        "plt.setp(legend.get_title(), fontsize=12)\n",
        "plt.setp(legend.get_texts(), fontsize=12)\n",
        "\n",
        "# Remove unnecessary spines for cleaner aesthetics\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "ax.spines['left'].set_linewidth(0.8)\n",
        "ax.spines['bottom'].set_linewidth(0.8)\n",
        "\n",
        "# Remove title\n",
        "ax.set_title('')\n",
        "\n",
        "# Formatting functions for the x-axes\n",
        "def format_with_comma(x, pos):\n",
        "    return f'{x*1000:,.0f}'\n",
        "\n",
        "def format_as_integer(x, pos):\n",
        "    return f'{x*1000:.0f}'\n",
        "\n",
        "# Apply the formatter to the primary x-axis\n",
        "ax.xaxis.set_major_formatter(FuncFormatter(format_with_comma))\n",
        "\n",
        "# Create secondary x-axis to display values scaled by the exchange rate\n",
        "secax = ax.secondary_xaxis('top', functions=(lambda x: x / exchange_rate_ecooking, lambda x: x * exchange_rate_ecooking))\n",
        "secax.set_xlabel('Average monthly cost in USD', fontsize=12)\n",
        "secax.tick_params(axis='x', labelsize=12)  # **Ensuring same font size for secondary x-axis labels**\n",
        "secax.xaxis.set_major_formatter(FuncFormatter(format_as_integer))\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig(fig_path + \"Monthly Fuel Source Cost by Connection Type.png\", dpi=500)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 0: Setup positions and styling\n",
        "bar_width = 0.4\n",
        "r = np.arange(len(connection_order))  # Bar positions\n",
        "\n",
        "expense_order = ['Electricity', 'Average monthly charcoal expense']\n",
        "expense_labels = ['Electricity', 'Charcoal']\n",
        "main_colors = ['#71B3FE', '#D7CFD6']\n",
        "\n",
        "burden_columns = ['electricity_burden', 'charcoal_burden']\n",
        "burden_labels = ['Electricity', 'Charcoal']\n",
        "burden_colors = ['#71B3FE', '#D7CFD6']\n",
        "\n",
        "# Step 1: Create subplot layout\n",
        "fig = plt.figure(figsize=(14, 4))\n",
        "gs = GridSpec(1, 2, width_ratios=[3, 1], wspace=0.03)\n",
        "ax_main = fig.add_subplot(gs[0])\n",
        "ax_burden = fig.add_subplot(gs[1], sharey=ax_main)\n",
        "\n",
        "# Step 2: Prepare burden data\n",
        "burden_df = expense_df[expense_df['Connection type'].isin(connection_type_mapping.keys())].copy()\n",
        "burden_df['Connection type'] = burden_df['Connection type'].map(connection_type_mapping)\n",
        "burden_df = burden_df.replace(999, np.nan)\n",
        "\n",
        "valid = (\n",
        "    burden_df['Electricity'].notna() &\n",
        "    burden_df['Average monthly charcoal expense'].notna() &\n",
        "    (total_expenses > 0)\n",
        ")\n",
        "\n",
        "burden_df['electricity_burden'] = burden_df['Electricity'] / total_expenses\n",
        "burden_df['charcoal_burden'] = burden_df['Average monthly charcoal expense'] / total_expenses\n",
        "\n",
        "burden_mean = burden_df.groupby('Connection type')[burden_columns].mean().reindex(connection_order) * 100\n",
        "burden_sem = burden_df.groupby('Connection type')[burden_columns].sem().reindex(connection_order) * 100\n",
        "\n",
        "# Step 3: Plot main monthly cost chart\n",
        "for i, (expense, label, color) in enumerate(zip(expense_order, expense_labels, main_colors)):\n",
        "    ax_main.barh(r + i * bar_width, mean_expenses[expense], xerr=sem_expenses[expense],\n",
        "                 height=bar_width, label=label, color=color, edgecolor='black', linewidth=0.5, capsize=5)\n",
        "\n",
        "ax_main.set_yticks(r + bar_width * (len(expense_order) - 1) / 2)\n",
        "ax_main.set_yticklabels(connection_order, fontsize=16)\n",
        "ax_main.set_xlabel('Average monthly cost in UGX', fontsize=16)\n",
        "ax_main.tick_params(axis='x', labelsize=16)\n",
        "ax_main.set_title('', fontsize=16)\n",
        "\n",
        "# Add black border around main plot\n",
        "for spine in ax_main.spines.values():\n",
        "    spine.set_linewidth(0.8)\n",
        "    spine.set_color('black')\n",
        "\n",
        "# Format UGX axis\n",
        "def format_with_comma(x, pos):\n",
        "    return f'{x*1000:,.0f}'\n",
        "\n",
        "def format_as_integer(x, pos):\n",
        "    return f'{x*1000:.0f}'\n",
        "\n",
        "ax_main.xaxis.set_major_formatter(FuncFormatter(format_with_comma))\n",
        "\n",
        "# USD axis\n",
        "secax = ax_main.secondary_xaxis('top', functions=(lambda x: x / exchange_rate_ecooking, lambda x: x * exchange_rate_ecooking))\n",
        "secax.set_xlabel('Average monthly cost in USD', fontsize=16)\n",
        "secax.tick_params(axis='x', labelsize=16)\n",
        "secax.xaxis.set_major_formatter(FuncFormatter(format_as_integer))\n",
        "\n",
        "# Step 4: Plot energy burden chart\n",
        "for i, (col, label, color) in enumerate(zip(burden_columns, burden_labels, burden_colors)):\n",
        "    ax_burden.barh(r + i * bar_width, burden_mean[col], xerr=burden_sem[col],\n",
        "                   height=bar_width, label=label, color=color, edgecolor='black', linewidth=0.5, capsize=5)\n",
        "\n",
        "ax_burden.set_xlabel('Energy burden', fontsize=16)\n",
        "ax_burden.tick_params(axis='x', labelsize=16)\n",
        "ax_burden.set_yticks([])  # Hide y-ticks, shared with ax_main\n",
        "ax_burden.set_title('', fontsize=16)\n",
        "\n",
        "# Add black border around burden plot\n",
        "for spine in ax_burden.spines.values():\n",
        "    spine.set_linewidth(0.8)\n",
        "    spine.set_color('black')\n",
        "\n",
        "# Format burden axis as 0–10% at 2% intervals\n",
        "def format_percentage(x, pos):\n",
        "    return f\"{x:.0f}%\"\n",
        "\n",
        "ax_burden.set_xlim(0, 10)\n",
        "ax_burden.set_xticks(np.arange(0, 11, 2))\n",
        "ax_burden.xaxis.set_major_formatter(FuncFormatter(format_percentage))\n",
        "\n",
        "# Restore y-axis tick labels for ax_burden\n",
        "ax_burden.set_yticks(r + bar_width * (len(expense_order) - 1) / 2)\n",
        "ax_burden.set_yticklabels(connection_order, fontsize=16)\n",
        "\n",
        "# Legend above second subplot (ax_burden)\n",
        "handles, labels = ax_main.get_legend_handles_labels()\n",
        "fig.legend(\n",
        "    handles, labels,\n",
        "    title='Fuel source',\n",
        "    loc='lower center',\n",
        "    bbox_to_anchor=(0.82, 0.95),\n",
        "    frameon=False,\n",
        "    fontsize=16,\n",
        "    title_fontsize=16,\n",
        "    ncol=1\n",
        ")\n",
        "\n",
        "# Force y-axis tick labels to appear only on the left (ax_main)\n",
        "ax_main.yaxis.set_tick_params(labelleft=True)\n",
        "ax_burden.yaxis.set_tick_params(labelleft=False)\n",
        "\n",
        "# Print number of valid survey respondents\n",
        "included_ids = burden_df.loc[valid, '_submission__id'].nunique()\n",
        "print(f\"(n = {included_ids}) survey respondents included in cost and burden analysis.\")\n",
        "\n",
        "# Final layout and save\n",
        "plt.tight_layout()\n",
        "plt.savefig(fig_path + \"Monthly Fuel Cost and Burden by Connection Type.png\", dpi=500, bbox_inches='tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "tpSLYmPYar1d",
        "outputId": "c2b2f44e-070a-4d9a-f9c4-8584160e1ab6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(n = 103) survey respondents included in cost and burden analysis.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-480-76086ee02f26>:116: UserWarning:\n",
            "\n",
            "This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x400 with 2 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Recalculate total_expenses if not already defined\n",
        "total_expenses = expense_df[expense_labels_full].sum(axis=1, min_count=1)\n",
        "\n",
        "# Define columns\n",
        "charcoal_col = 'Average monthly charcoal expense'\n",
        "electricity_col = 'Electricity'\n",
        "connection_col = 'Connection type'\n",
        "\n",
        "# Create energy burdens relative to total expenses\n",
        "expense_df['electricity_burden'] = expense_df[electricity_col] / total_expenses\n",
        "expense_df['charcoal_burden'] = expense_df[charcoal_col] / total_expenses\n",
        "\n",
        "# Filter to valid entries and target connection types\n",
        "valid_burden = (\n",
        "    total_expenses.notna() &\n",
        "    (total_expenses > 0) &\n",
        "    (expense_df[connection_col].isin([2, 5, 6]))\n",
        ")\n",
        "\n",
        "# Group and summarize\n",
        "burden_summary = (\n",
        "    expense_df[valid_burden]\n",
        "    .groupby(connection_col)\n",
        "    .agg(\n",
        "        n=('electricity_burden', 'count'),\n",
        "        avg_electricity_burden=('electricity_burden', 'mean'),\n",
        "        avg_charcoal_burden=('charcoal_burden', 'mean')\n",
        "    )\n",
        "    .reset_index()\n",
        ")\n",
        "\n",
        "# Format as percentages\n",
        "burden_summary['avg_electricity_burden'] *= 100\n",
        "burden_summary['avg_charcoal_burden'] *= 100\n",
        "\n",
        "# Round for presentation\n",
        "burden_summary = burden_summary.round({\n",
        "    'avg_electricity_burden': 2,\n",
        "    'avg_charcoal_burden': 2\n",
        "})\n",
        "\n",
        "display(burden_summary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "ZeKEowfJZ0nZ",
        "outputId": "0968b65f-654d-43f2-8a29-51726075b849"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Connection type   n  avg_electricity_burden  avg_charcoal_burden\n",
              "0                2  34                    5.08                 7.87\n",
              "1                5  62                    3.71                 8.26\n",
              "2                6  10                    3.34                 8.81"
            ],
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              "      <th>Connection type</th>\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4e33210a-88bb-4a91-9197-eabaf800ffaf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4e33210a-88bb-4a91-9197-eabaf800ffaf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-70e62725-a82b-449d-a9d7-8c482f3c7578\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-70e62725-a82b-449d-a9d7-8c482f3c7578')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-70e62725-a82b-449d-a9d7-8c482f3c7578 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_6dc37584-8b73-4c5e-92b8-c07e3a68c645\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('burden_summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_6dc37584-8b73-4c5e-92b8-c07e3a68c645 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('burden_summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "burden_summary",
              "summary": "{\n  \"name\": \"burden_summary\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Connection type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2,\n        \"max\": 6,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          5,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 10,\n        \"max\": 62,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          34,\n          62,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_electricity_burden\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9166424239218549,\n        \"min\": 3.34,\n        \"max\": 5.08,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.08,\n          3.71,\n          3.34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_charcoal_burden\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4722640504350649,\n        \"min\": 7.87,\n        \"max\": 8.81,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7.87,\n          8.26,\n          8.81\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8KLlvRpqPQGm"
      },
      "source": [
        "## Expenses by fuel source, all respondents"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "oCdYshNfOeOE",
        "outputId": "a6673def-bc60-44f5-f9f2-7e9b81374fed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average monthly expense:  92.05528455284555\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define the expense categories to include in the stacked bar chart\n",
        "expense_order = [\n",
        "    'Electricity',\n",
        "    'Average monthly firewood expense',\n",
        "    'Average monthly charcoal expense',\n",
        "    'Average monthly gas expense'\n",
        "]\n",
        "\n",
        "# Define the colors for each category\n",
        "colors = {\n",
        "    'Electricity': '#71b3ff',\n",
        "    'Average monthly firewood expense': '#372e29',\n",
        "    'Average monthly charcoal expense': '#bc9ea0',\n",
        "    'Average monthly gas expense': '#ffc000'\n",
        "}\n",
        "\n",
        "# Rename legend labels for clarity\n",
        "legend_labels = {\n",
        "    'Electricity': 'Electricity',\n",
        "    'Average monthly firewood expense': 'Firewood',\n",
        "    'Average monthly charcoal expense': 'Charcoal',\n",
        "    'Average monthly gas expense': 'Gas'\n",
        "}\n",
        "\n",
        "# Filter the DataFrame to include only the necessary columns and replace NaN with 0\n",
        "df_stacked = expense_df[expense_order].fillna(0)\n",
        "\n",
        "# Add a 'Total Expense' column to sort by total expenses\n",
        "df_stacked['Total Expense'] = df_stacked.sum(axis=1)\n",
        "\n",
        "# Sort the DataFrame in descending order of total expenses\n",
        "df_stacked = df_stacked.sort_values(by='Total Expense', ascending=False)\n",
        "\n",
        "# Remove the 'Total Expense' column after sorting\n",
        "df_stacked = df_stacked.drop(columns=['Total Expense'])\n",
        "\n",
        "# Font size parameters\n",
        "title_fontsize = 14\n",
        "tick_fontsize = 14\n",
        "legend_fontsize = 14\n",
        "\n",
        "# Calculate the average monthly expense across all respondents\n",
        "average_monthly_expense = df_stacked.sum(axis=1).mean()\n",
        "\n",
        "# Create a figure and axis\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Create the stacked bar chart\n",
        "x = range(len(df_stacked))  # Each respondent as an individual bar\n",
        "bottom = None  # Initialize the bottom for stacking\n",
        "for expense in expense_order:\n",
        "    ax1.bar(x, df_stacked[expense], bottom=bottom, label=legend_labels[expense], color=colors[expense])\n",
        "    bottom = df_stacked[expense] if bottom is None else bottom + df_stacked[expense]\n",
        "\n",
        "# Draw a thin black horizontal line at the average monthly expense\n",
        "average_line = ax1.axhline(average_monthly_expense, color='black', linestyle='-', linewidth=1)\n",
        "\n",
        "# Format the left y-axis (raw numbers)\n",
        "ax1.set_ylabel('Average monthly expense (1,000 UGX)', fontsize=title_fontsize)\n",
        "ax1.set_xlabel('Respondents', fontsize=title_fontsize)\n",
        "ax1.tick_params(axis='y', labelsize=tick_fontsize)\n",
        "ax1.tick_params(axis='x', labelsize=tick_fontsize)\n",
        "ax1.set_xlim(-1, len(df_stacked))  # Specify x-extents\n",
        "ax1.set_xticks([])  # Remove x-tick labels\n",
        "\n",
        "# Create a secondary y-axis for USD\n",
        "ax2 = ax1.twinx()\n",
        "ax2.set_ylabel('Average monthly expense (USD)', fontsize=title_fontsize)\n",
        "ax2.set_ylim(ax1.get_ylim()[0] / exchange_rate_ecooking, ax1.get_ylim()[1] / exchange_rate_ecooking * 1000)\n",
        "ax2.tick_params(axis='y', labelsize=tick_fontsize)\n",
        "\n",
        "# Create custom legend handles\n",
        "bar_handles = [mpatches.Patch(color=colors[expense], label=legend_labels[expense]) for expense in expense_order]\n",
        "line_handle = mlines.Line2D([], [], color='black', linestyle='-', linewidth=1, label=\"Average total monthly expense\")\n",
        "\n",
        "# Add the legend with renamed entries and include the label for \"Average monthly expense\"\n",
        "legend = ax1.legend(\n",
        "    handles=bar_handles + [line_handle],  # Ensure patches and line match correctly\n",
        "    fontsize=legend_fontsize,\n",
        "    frameon=False,\n",
        "    loc='upper right'\n",
        ")\n",
        "\n",
        "# Adjust layout and show the plot\n",
        "plt.tight_layout()\n",
        "print('Average monthly expense: ', average_monthly_expense)\n",
        "\n",
        "plt.savefig(fig_path + \"Monthly Fuel Cost Distribution.png\", dpi=500)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MkD4PzPh9O70"
      },
      "source": [
        "## Fuel cost comparison"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "JxJMlBfP9VbZ",
        "outputId": "a447c715-6682-4853-f772-890a06b5d8db"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define constants\n",
        "charcoal_EPC_ratio = 14\n",
        "charcoal_hotplate_ratio = 10\n",
        "kWh_to_MJ = 3.6\n",
        "charcoal_cost = 0.286  # USD/kg\n",
        "charcoal_energy_content = 24.9  # MJ/kg\n",
        "electricity_cost = 0.21  # USD/kWh\n",
        "exchange_rate = exchange_rate_ecooking  # UGX/USD\n",
        "\n",
        "# Calculate cost ratios\n",
        "charcoal_EPC_cost_ratio = (kWh_to_MJ * charcoal_EPC_ratio * charcoal_cost) / (charcoal_energy_content * electricity_cost)\n",
        "charcoal_hotplate_cost_ratio = (kWh_to_MJ * charcoal_hotplate_ratio * charcoal_cost) / (charcoal_energy_content * electricity_cost)\n",
        "\n",
        "# Define x-axis: cost of electricity in USD/kWh\n",
        "x_values = np.linspace(0.05, 0.40, 100)\n",
        "\n",
        "# Calculate y-values\n",
        "epc_y_values = charcoal_EPC_cost_ratio * x_values\n",
        "hotplate_y_values = charcoal_hotplate_cost_ratio * x_values\n",
        "\n",
        "# Plotting\n",
        "fig, ax = plt.subplots(figsize=(12, 7))\n",
        "\n",
        "# Plot EPC and Hot Plate lines\n",
        "ax.plot(x_values, epc_y_values, label=\"EPC\", color=\"#e3555b\", linewidth=2)\n",
        "ax.plot(x_values, hotplate_y_values, label=\"Hot plate\", color=\"#2E86C1\", linewidth=2)\n",
        "\n",
        "# Vertical line at current electricity cost (now in legend)\n",
        "ax.axvline(x=0.21, color=\"black\", linestyle=\"-\", linewidth=1, label=\"Average cost of electricity\")\n",
        "\n",
        "# Axes labels and limits\n",
        "ax.set_xlabel(\"Cost of using 1 kWh for e-cooking (USD/kWh)\", fontsize=16)\n",
        "ax.set_ylabel(\"Equivalent cost of cooking with charcoal (USD)\", fontsize=16)\n",
        "ax.set_xlim(0.05, 0.4)\n",
        "ax.set_ylim(0, 1.25)\n",
        "\n",
        "# Formatter for comma-separated UGX\n",
        "ugx_formatter = ticker.FuncFormatter(lambda x, _: f\"{int(x):,}\")\n",
        "\n",
        "# Secondary x-axis (UGX)\n",
        "secax_x = ax.secondary_xaxis('top', functions=(lambda x: x * exchange_rate, lambda x: x / exchange_rate))\n",
        "secax_x.set_xlabel(\"Cost of using 1 kWh for e-cooking (UGX/kWh)\", fontsize=16)\n",
        "secax_x.xaxis.set_major_formatter(ugx_formatter)\n",
        "secax_x.tick_params(axis='x', labelsize=14)\n",
        "\n",
        "# Secondary y-axis (UGX)\n",
        "secax_y = ax.secondary_yaxis('right', functions=(lambda y: y * exchange_rate, lambda y: y / exchange_rate))\n",
        "secax_y.set_ylabel(\"Equivalent cost of cooking with charcoal (UGX)\", fontsize=16)\n",
        "secax_y.yaxis.set_major_formatter(ugx_formatter)\n",
        "secax_y.tick_params(axis='y', labelsize=14)\n",
        "\n",
        "# Format and grid\n",
        "ax.tick_params(axis='both', labelsize=14)\n",
        "ax.grid(True, linestyle='-', color='gray', alpha=0.4)\n",
        "\n",
        "# Legend with white background and no border\n",
        "ax.legend(\n",
        "    loc=\"upper left\",\n",
        "    fontsize=14,\n",
        "    frameon=True,\n",
        "    facecolor='white',\n",
        "    edgecolor='none'\n",
        ")\n",
        "\n",
        "# Layout\n",
        "plt.tight_layout()\n",
        "\n",
        "# Save and display\n",
        "plt.savefig(fig_path + \"Fuel cost comparison.png\", dpi=500)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Electricity cost of interest\n",
        "x_point = 0.21\n",
        "\n",
        "# Calculate cost per meal at this electricity price\n",
        "epc_cost_at_021 = charcoal_EPC_cost_ratio * x_point\n",
        "hotplate_cost_at_021 = charcoal_hotplate_cost_ratio * x_point\n",
        "\n",
        "# Convert to cost per MJ of useful energy\n",
        "epc_cost_per_MJ = epc_cost_at_021 / x_point\n",
        "hotplate_cost_per_MJ = hotplate_cost_at_021 / x_point\n",
        "\n",
        "# Print results\n",
        "print(f\"EPC cost ratio at ${x_point}/kWh: {epc_cost_per_MJ:.3f}\")\n",
        "print(f\"Hot plate cost ratio at ${x_point}/kWh: {hotplate_cost_per_MJ:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YGvay3iT2lSc",
        "outputId": "1e06ae86-50de-4c60-abe2-88dbe2ec4bd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPC cost ratio at $0.21/kWh: 2.757\n",
            "Hot plate cost ratio at $0.21/kWh: 1.969\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "foTTMM-C9POG"
      },
      "source": [
        "# Appliance/fuel source usage"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eXbPaseMuJOx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "67b91f21-cd66-4649-ca28-2cc0a2daa129"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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2+Ph4I1++fEbNmjXNP3uGcf/nIXfu3MYbb7yR7GU9yvr16w1Jxo8//mjRXqJECSNbtmzGlStXLLYhXbp0Rps2bcxtgwYNMiQZLVu2fKb1Pah48eJGpkyZzO8TfjZOnjxpbmvbtq3h5uaWaN7g4GCjSJEiFm1Pc7yuWLHCou8XX3xhuLm5GUePHrVo79evn+Hg4GCcOXPGMAzDOHnypCHJyJIli3H16lVzv2XLlhmSjF9//dXc1q1bNyO5v7pNnDjRkGQsWbIkWf179eplSLI4xq9fv27kzp3bCAgIMOLi4gzDMIwJEyYYkox58+aZ+925c8coX7684e7ubkRHR1ts15gxY4y7d+8azZs3N1xcXIyVK1darDdh/z74PZbwXfvdd9+Z22JjYw0fHx+jcePG5rZx48YZkoylS5ea227dumUULFjwkd+ND0r4OUnq5eTkZO5nzT5K7ndNwrpff/114969exbbmdzv5ZUrVxqSjOXLl1tsV7FixSz6JeXmzZtGgQIFDElGrly5jJCQEOObb74x/v3330R9k3tst23b1siVK1ei+ROO+QdJMtKlS2ccPHjQor1NmzZGunTpkvx/QMJ3WnKPrZ49exoeHh4Wny9gT1xqDiDNq1GjhrJmzSo/Pz+1aNFC7u7uWrJkiXx9ffXzzz8rPj5ezZo10+XLl80vHx8f5cuXL9GZEycnJ7Vr186i7aeffpK3t7fef//9ROtOOIvw448/ytPTU2+88YbFekqXLi13d/dE6ylcuLD5kmlJypo1qwoUKKATJ04ka5sfvL/0+vXrunz5sipVqqSbN2/q8OHDkqRdu3bpypUrevfddy3u82zVqpXF2eKE+gsVKqSCBQta1J9w2f7jzjDt2rVLFy9eVNeuXS3u36tTp44KFiyY5KWs1nhw/zZp0kRubm765ZdfzGeGrl69qnXr1qlZs2bmz+Ly5cu6cuWKatasqcjISPMl715eXjp48KAiIyOfuN7u3bub/51w2eSdO3e0Zs0aSfcHwnJwcFCPHj0s5uvdu7cMw9Dy5csTbceDZ/iLFSsmDw8Pi33u5eWl7du3P3LE4fDwcEVGRup///ufrly5Yt7WGzduqHr16tq0aZP5UswnLcsa58+fV3h4uEJCQpQ5c2aLbXjjjTf0xx9/JJqnS5cuz7zeBO7u7rp+/XqKLc/a4zV37tyqWbNmomVUqlRJmTJlslhGjRo1FBcXp02bNln0b968ucVxl3D8J/eYf1h0dLQkKWPGjMnq/8cff6hcuXIWl/27u7urU6dOOnXqlA4dOmTu5+PjYzE+RoYMGdSjRw/FxMRo48aNFsu9c+eOmjZtqt9++01//PGH3nzzzWTV4+7urnfeecf83tHRUeXKlbP4PFasWCFfX1/Vq1fP3Obs7Kx33303WetIMHXqVK1evdri9fDxKT15H1nzXZPg3XfftThbbc33co0aNZQzZ07Nnz/f3HbgwAHt27fP4rNLiouLi7Zv366PPvpI0v2rkjp06KAcOXLo/fffN99C9DTHdnIFBwercOHC5vfx8fFaunSp6tatm+Q99w/+/zQ5x5aXl5du3LhhcasQYE9cag4gzZs6dary58+v9OnTK3v27CpQoIDSpbv/d8fIyEgZhqF8+fIlOe+Dl5NKkq+vb6LBmY4fP64CBQo8dpCiyMhIRUVFKVu2bElOTxiwKoG/v3+iPpkyZUp0f+mjHDx4UJ999pnWrVtn/gU8QVRUlKT790ZKiUfuTZ8+faJLByMjIxUREaGsWbMmq/4HJaynQIECiaYVLFhQW7ZsefzGPEHC/o2KitK3336rTZs2WYxmfezYMRmGoQEDBmjAgAGPrN/X11eff/656tevr/z586to0aKqVauWWrdurWLFiln0T5cunfLkyWPRlj9/fkn/f6n76dOnlTNnzkTBJ+FS5oTPJUFy9vno0aPVtm1b+fn5qXTp0nrrrbfUpk0bcy0JfzBIuFc+KVFRUcqUKdMTl2WNx+3jQoUKaeXKlYkGjkpqFOmnFRMTk+yAmRzWHq9JbUtkZKT27duX7GPm4f2fELKSe8w/LOEe3eT+QeL06dN69dVXE7U/+PNatGhRnT59Wvny5TN/hybV70EjRoxQTEyMli9fnmjciMd55ZVXEl3+nClTJu3bt8+i5sDAwET9rB2NvFy5cskaXO1J+8ia75oED//sWPO9nC5dOrVq1UrTpk0zD/Q5f/58OTs7m8cAeRxPT0+NHj1ao0eP1unTp7V27VqNHTtWU6ZMkaenp4YOHfpUx3ZyPbztly5dUnR0tIoWLfrY+ZJ7bHXt2lWLFi1S7dq15evrqzfffFPNmjVTrVq1rK4VSAkEbwBp3uN+qYqPj5fJZNLy5cuTvA/S3d3d4v3TjlQcHx+vbNmyWZyZeNDDv0A86p5MIxn3wl67dk3BwcHy8PDQ559/rsDAQDk7O+uvv/7Sxx9/nGjwmeTWHxQUpPHjxyc53c/Pz+plppQH92+DBg30+uuv63//+5+OHDkid3d38/b26dMn0VnJBAm/5FauXFnHjx/XsmXLtGrVKs2aNUtffvmlpk+fbvNHZyVnnzdr1kyVKlXSkiVLtGrVKo0ZM0ajRo3Szz//rNq1a5u3dcyYMSpRokSSy0v4mX7SsmwtpUb9vnv3ro4ePfrEX9atYe3xmtS2xMfH64033lDfvn2TXEbCH2oSPMsxn5SCBQtKkvbv368GDRo81TJSQs2aNbVixQqNHj1aVapUSfao1Sn9eaSEJ9VkzXdNgmc9Dtq0aaMxY8Zo6dKlatmypb7//nu9/fbb8vT0tGo5uXLlUvv27dWwYUPlyZNH8+fP19ChQ61axqPuE09qUE/p2f5/mpxjK1u2bAoPD9fKlSu1fPlyLV++XLNnz1abNm2SNRggkNII3gBeaoGBgTIMQ7lz5070i7A1y9i+fbvu3r2b6Az5g33WrFmjihUrpljgeNQvORs2bNCVK1f0888/q3Llyub2B0dxl/5/cJ1jx46patWq5vZ79+7p1KlTFmd5AwMDtXfvXlWvXv2xg/AkJWE9R44csXjWckJbwvSUkDCSb9WqVTVlyhT169fPfAY3Q4YMyXo2eObMmdWuXTu1a9dOMTExqly5sgYPHmwRvOPj43XixAmLn5mjR49KkvmsVK5cubRmzRpdv37d4mxswqX+T7vdOXLkUNeuXdW1a1ddvHhRpUqV0rBhw1S7dm3zpeoeHh7J2tbHLcsaD+7jhx0+fFje3t42e0zS4sWLdevWrUcGnaeREsdrYGCgYmJikv08+uSw5th7/fXXlSlTJv3www/65JNPnjjAWq5cuR65/xKmJ/x33759io+Ptzjr/aif69dee01dunTR22+/raZNm2rJkiUp9gizXLly6dChQzIMw+KzOXbsWIos31rWftckxZrvZUkqWrSoSpYsqfnz5+uVV17RmTNnNHny5Kfcgvtn8QMDA3XgwAGLepJzbGfKlEnXrl1L1O/hqyAeJWvWrPLw8DCv+1GsObYcHR1Vt25d1a1bV/Hx8eratau+/vprDRgwIE0/px0vJu7xBvBSa9SokRwcHDRkyJBEZ1IMw9CVK1eeuIzGjRvr8uXLFo8zenAZ0v2zi3Fxcfriiy8S9bl3716Sv6w8ScIvOw/Pm/AL9oPbc+fOHX311VcW/cqUKaMsWbJo5syZunfvnrl9/vz5iS5vbdasmc6dO6eZM2cmquPWrVvm0XiTUqZMGWXLlk3Tp0+3ePTY8uXLFRER8cTRja1VpUoVlStXThMmTNDt27eVLVs2ValSRV9//bXOnz+fqP+lS5fM/354f7u7uytv3rxJPjLtwf1tGIamTJmiDBkyqHr16pKkt956S3FxcYl+Lr788kuZTCarw21cXJz5NoEE2bJlU86cOc31lS5dWoGBgRo7dqxiYmIeua3JWZY1cuTIoRIlSmjOnDkWP48HDhzQqlWr9NZbb1m9zOTYu3evevXqpUyZMqlbt24pttyUOF6bNWumbdu2aeXKlYmmXbt2zeKYS65HHfNJcXV11ccff6yIiAh9/PHHSZ4pnjdvnnbs2CHp/s/rjh07tG3bNvP0GzduaMaMGQoICDDfi/vWW2/pwoULWrhwobnfvXv3NHnyZLm7u5sfN/egGjVqaMGCBVqxYoVat279VFfdJKVmzZo6d+6cxaO6bt++neT31PNgzXfNo1jzvZygdevWWrVqlSZMmKAsWbIk67tl7969iUZyl+6H5EOHDpkvLbfm2A4MDFRUVJTF7QDnz5/XkiVLnliPdP/S+QYNGujXX3/Vrl27Ek1/8P+nyTm2Hv4+T5cunfkPF0/zPQc8K854A3ipBQYGaujQoerfv7/5kS0ZM2bUyZMntWTJEnXq1El9+vR57DLatGmj7777Th9++KF27NihSpUq6caNG1qzZo26du2q+vXrKzg4WJ07d9aIESMUHh6uN998UxkyZFBkZKR+/PFHTZw4UU2aNLGq9hIlSsjBwUGjRo1SVFSUnJycVK1aNVWoUEGZMmVS27Zt1aNHD5lMJs2dOzfRL96Ojo4aPHiw3n//fVWrVk3NmjXTqVOnFBoamui+ydatW2vRokXq0qWL1q9fr4oVKyouLk6HDx/WokWLzM8wTkqGDBk0atQotWvXTsHBwWrZsqX5cWIBAQH64IMPrNru5Pjoo4/UtGlThYaGqkuXLpo6dapef/11BQUF6d1331WePHn077//atu2bfr777+1d+9eSfcHtatSpYpKly6tzJkza9euXeZHbj3I2dlZK1asUNu2bfXqq69q+fLl+v333/XJJ5+YL0OuW7euqlatqk8//VSnTp1S8eLFtWrVKi1btky9evWy+lFp169f1yuvvKImTZqoePHicnd315o1a7Rz506NGzdO0v1fLGfNmqXatWurSJEiateunXx9fXXu3DmtX79eHh4e+vXXX5O1LGuNGTNGtWvXVvny5dWhQwfzI4c8PT01ePDgp1rmgzZv3qzbt28rLi5OV65cUVhYmH755Rd5enpqyZIl8vHxeeZ1JEiJ4/Wjjz7SL7/8orffftv8KMAbN25o//79Wrx4sU6dOiVvb2+r6ipdurQkqUePHqpZs6YcHBzUokWLx9Zw8OBBjRs3TuvXr1eTJk3k4+OjCxcuaOnSpdqxY4e2bt0qSerXr59++OEH1a5dWz169FDmzJk1Z84cnTx5Uj/99JP57HanTp309ddfKyQkRLt371ZAQIAWL16ssLAwTZgw4ZH32jdo0MB8ma+Hh4f5WefPonPnzpoyZYpatmypnj17KkeOHOZ7nKXkXyGwfPly8xn7B1WoUMHqMQ+S+13zKNZ8Lyf43//+p759+2rJkiV67733Hnnl1YNWr16tQYMGqV69enrttdfk7u6uEydO6Ntvv1VsbKzFMZvcY7tFixb6+OOP1bBhQ/Xo0UM3b97UtGnTlD9/fv3111/J+vyGDx+uVatWKTg42PzoyvPnz+vHH3/Uli1b5OXllexjq2PHjrp69aqqVaumV155RadPn9bkyZNVokQJ85gEwHP1nEdRB4DnxppHU/3000/G66+/bri5uRlubm5GwYIFjW7duhlHjhwx90nqsUMJbt68aXz66adG7ty5jQwZMhg+Pj5GkyZNjOPHj1v0mzFjhlG6dGnDxcXFyJgxoxEUFGT07dvX+Oeff8x9cuXKleQjrYKDgxM9ImbmzJlGnjx5DAcHB4vH54SFhRmvvfaa4eLiYuTMmdPo27ev+dEzDz9iZ9KkSUauXLkMJycno1y5ckZYWJhRunRpo1atWhb97ty5Y4waNcooUqSI4eTkZGTKlMkoXbq0MWTIECMqKupJH7GxcOFCo2TJkoaTk5OROXNmo1WrVsbff/9t0edpHieWVN+4uDgjMDDQCAwMND9K5vjx40abNm0MHx8fI0OGDIavr6/x9ttvG4sXLzbPN3ToUKNcuXKGl5eX4eLiYhQsWNAYNmyYcefOHXOfhMdSHT9+3HjzzTcNV1dXI3v27MagQYPMj1xKcP36deODDz4wcubMaWTIkMHIly+fMWbMGItHfRnG/UfrJPVor1y5chlt27Y1DOP+Y4Y++ugjo3jx4kbGjBkNNzc3o3jx4sZXX32VaL49e/YYjRo1MrJkyWI4OTkZuXLlMpo1a2asXbvW6mU97HGP91qzZo1RsWJFw8XFxfDw8DDq1q1rHDp0yKJPwqOFEh67ltz1JbwyZMhgZM2a1ahcubIxbNgw4+LFi4nmedbHiSV4luPVMO7v//79+xt58+Y1HB0dDW9vb6NChQrG2LFjzT9TDz5262GSjEGDBpnf37t3z3j//feNrFmzGiaTKdmPFlu8eLHx5ptvGpkzZzbSp09v5MiRw2jevLmxYcMGi37Hjx83mjRpYnh5eRnOzs5GuXLljN9++y3R8v7991+jXbt2hre3t+Ho6GgEBQUZs2fPtujzqO366quvDElGnz59DMN49OPEktonST2u6sSJE0adOnUMFxcXI2vWrEbv3r2Nn376yZBk/Pnnn4/9XB73ODFJ5m2yZh8ZRvK+a570XZfc7+UEb731liHJ2Lp162O3OcGJEyeMgQMHGq+99pqRLVs2I3369EbWrFmNOnXqGOvWrUvUPznHtmEYxqpVq4yiRYsajo6ORoECBYx58+Y98nFij3qc4enTp402bdqYHw2ZJ08eo1u3bkZsbKy5T3KOrYSf+2zZshmOjo6Gv7+/0blzZ+P8+fPJ+oyAlGYyDDuOUgEAeOHEx8cra9asatSokd0u2XyRhYSEaPHixUleyg3A/iZMmKAPPvhAf//9t8UI4qnZk76XGzZsqP3799vt/nYAT8Y93gDwErt9+3aiS9C/++47Xb161apH/wCAPdy6dcvi/e3bt/X1118rX758qTZ0W/u9fP78ef3+++9q3br1c6oQwNPgHm8AeIn9+eef+uCDD9S0aVNlyZJFf/31l7755hsVLVo0Wc+BBQB7atSokfz9/VWiRAlFRUVp3rx5Onz48CMfBZcaJPd7+eTJkwoLC9OsWbOUIUMGde7c2Y5VA3gSgjcAvMQCAgLk5+enSZMm6erVq8qcObPatGmjkSNHytHR0d7lAcBj1axZU7NmzdL8+fMVFxenwoULa8GCBWrevLm9S3tqyf1e3rhxo9q1ayd/f3/NmTMnRQcYBJDyuMcbAAAAAAAb4h5vAAAAAABsiOANAAAAAIANcY83Xhjx8fH6559/lDFjRplMJnuXAwAAAACPZBiGrl+/rpw5cypdusef0yZ444Xxzz//yM/Pz95lAAAAAECynT17Vq+88spj+xC88cLImDGjpPs/uB4eHnauBgAAAAAeLTo6Wn5+fuYc8zgEb7wwEi4v9/DwIHgDAAAASBWSc5ssg6sBAAAAAGBDBG8AAAAAAGyI4A0AAAAAgA0RvAEAAAAAsCGCNwAAAAAANkTwBgAAAADAhgjeAAAAAADYEMEbAAAAAAAbIngDAAAAAGBDBG8AAAAAAGyI4A0AAAAAgA0RvAEAAAAAsKH09i4AeFh4eLjc3d3tXQYAAACAF4C3t7f8/f3tXcYzIXjjhRMcHGzvEgAAAAC8IFxcXXU4IiJVh2+CN144DT8bL99CxexdBgAAAAA7u3gyUos+e0+XL18meAMpKWuuQPkWKm7vMgAAAAAgRTC4GgAAAAAANkTwBgAAAADAhgjeAAAAAADYEMEbAAAAAAAbIngDAAAAAGBDBG8AAAAAAGyI4A0AAAAAgA0RvF9wJpNJS5cutXcZTyU0NFReXl72LgMAAAAA7IrgbWcXLlzQ+++/rzx58sjJyUl+fn6qW7eu1q5da+/SAAAAAAApIL29C3iZnTp1ShUrVpSXl5fGjBmjoKAg3b17VytXrlS3bt10+PBhm6z3zp07cnR0tMmyAQAAAACWOONtR127dpXJZNKOHTvUuHFj5c+fX0WKFNGHH36oP//809zv8uXLatiwoVxdXZUvXz798ssv5mlxcXHq0KGDcufOLRcXFxUoUEATJ060WE9ISIgaNGigYcOGKWfOnCpQoIAk6e+//1bLli2VOXNmubm5qUyZMtq+fbt5vmnTpikwMFCOjo4qUKCA5s6da7Hc8ePHKygoSG5ubvLz81PXrl0VExNji48KAAAAAFItgredXL16VStWrFC3bt3k5uaWaPqD90YPGTJEzZo10759+/TWW2+pVatWunr1qiQpPj5er7zyin788UcdOnRIAwcO1CeffKJFixZZLG/t2rU6cuSIVq9erd9++00xMTEKDg7WuXPn9Msvv2jv3r3q27ev4uPjJUlLlixRz5491bt3bx04cECdO3dWu3bttH79evMy06VLp0mTJungwYOaM2eO1q1bp759+9rg0wIAAACA1ItLze3k2LFjMgxDBQsWfGLfkJAQtWzZUpI0fPhwTZo0STt27FCtWrWUIUMGDRkyxNw3d+7c2rZtmxYtWqRmzZqZ293c3DRr1izzJeYzZszQpUuXtHPnTmXOnFmSlDdvXnP/sWPHKiQkRF27dpUk81n4sWPHqmrVqpKkXr16mfsHBARo6NCh6tKli7766qtkfQaxsbGKjY01v4+Ojk7WfAAAAACQmnDG204Mw0h232LFipn/7ebmJg8PD128eNHcNnXqVJUuXVpZs2aVu7u7ZsyYoTNnzlgsIygoyOK+7vDwcJUsWdIcuh8WERGhihUrWrRVrFhRERER5vdr1qxR9erV5evrq4wZM6p169a6cuWKbt68maztGjFihDw9Pc0vPz+/ZM0HAAAAAKkJwdtO8uXLJ5PJlKwB1DJkyGDx3mQymS8JX7Bggfr06aMOHTpo1apVCg8PV7t27XTnzh2LeR6+nN3FxeWZ6j916pTefvttFStWTD/99JN2796tqVOnSlKidT9K//79FRUVZX6dPXv2mWoCAAAAgBcRwdtOMmfOrJo1a2rq1Km6ceNGounXrl1L1nLCwsJUoUIFde3aVSVLllTevHl1/PjxJ85XrFgxhYeHm+8Vf1ihQoUUFhaWaF2FCxeWJO3evVvx8fEaN26cXnvtNeXPn1///PNPsmpO4OTkJA8PD4sXAAAAAKQ1BG87mjp1quLi4lSuXDn99NNPioyMVEREhCZNmqTy5csnaxn58uXTrl27tHLlSh09elQDBgzQzp07nzhfy5Yt5ePjowYNGigsLEwnTpzQTz/9pG3btkmSPvroI4WGhmratGmKjIzU+PHj9fPPP6tPnz6S7t8PfvfuXU2ePFknTpzQ3LlzNX369Kf/MAAAAAAgjSJ421GePHn0119/qWrVqurdu7eKFi2qN954Q2vXrtW0adOStYzOnTurUaNGat68uV599VVduXLFPCDa4zg6OmrVqlXKli2b3nrrLQUFBWnkyJFycHCQJDVo0EATJ07U2LFjVaRIEX399deaPXu2qlSpIkkqXry4xo8fr1GjRqlo0aKaP3++RowY8dSfBQAAAACkVSbDmlG+ABuKjo6Wp6enOs1cptylK9i7HAAAAAB2di5ir6a0qqHdu3erVKlS9i7HQkJ+iYqKeuJts5zxBgAAAADAhgjeAAAAAADYEMEbAAAAAAAbIngDAAAAAGBDBG8AAAAAAGyI4A0AAAAAgA0RvAEAAAAAsCGCNwAAAAAANkTwBgAAAADAhtLbuwDgYZdOH5ejq5u9ywAAAABgZxdPRtq7hBRB8MYLZ8nQD+1dAgAAAIAXhIurq7y9ve1dxjMheOOFs3HjRrm7u9u7DAAAAAAvAG9vb/n7+9u7jGdC8MYLp0SJEvLw8LB3GQAAAACQIhhcDQAAAAAAGyJ4AwAAAABgQwRvAAAAAABsiOANAAAAAIANEbwBAAAAALAhgjcAAAAAADZE8AYAAAAAwIYI3gAAAAAA2BDBGwAAAAAAGyJ4AwAAAABgQwRvAAAAAABsiOANAAAAAIANEbwBAAAAALAhgjcAAAAAADZE8AYAAAAAwIYI3gAAAAAA2BDBGwAAAAAAGyJ4AwAAAABgQwRvAAAAAABsiOANAAAAAIANEbwBAAAAALAhgjcAAAAAADZE8AYAAAAAwIYI3gAAAAAA2BDBGwAAAAAAGyJ4AwAAAABgQwRvAAAAAABsKL29CwAeFh4eLnd3d3uXATxX3t7e8vf3t3cZAAAAsAGCN144wcHB9i4BeO5cXF11OCKC8A0AAJAGEbzxwmn42Xj5Fipm7zKA5+biyUgt+uw9Xb58meANAACQBhG88cLJmitQvoWK27sMAAAAAEgRDK4GAAAAAIANEbwBAAAAALAhgjcAAAAAADZE8AYAAAAAwIYI3gAAAAAA2BDBGwAAAAAAGyJ4AwAAAABgQwTvJGzYsEEmk0nXrl17ZJ/Q0FB5eXk903pSYhlP69SpUzKZTAoPD09W/5CQEDVo0MCmNQEAAABAWpRmg/eFCxfUs2dP5c2bV87OzsqePbsqVqyoadOm6ebNm4+dt0KFCjp//rw8PT2fqQaTyZTka8GCBc+03EexJsj7+fnp/PnzKlq0aLL6T5w4UaGhoeb3VapUUa9evawvEgAAAABeMuntXYAtnDhxQhUrVpSXl5eGDx+uoKAgOTk5af/+/ZoxY4Z8fX1Vr169JOe9e/euHB0d5ePjkyK1zJ49W7Vq1bJos9dZ7gR37tyxehuf9Y8QAAAAAPCySpNnvLt27ar06dNr165datasmQoVKqQ8efKofv36+v3331W3bl1zX5PJpGnTpqlevXpyc3PTsGHDkrzUPDQ0VP7+/nJ1dVXDhg115cqVZNXi5eUlHx8fi5ezs/Mj+y9btkylSpWSs7Oz8uTJoyFDhujevXvm6deuXVPnzp2VPXt2OTs7q2jRovrtt9+0YcMGtWvXTlFRUeYz64MHD5YkBQQE6IsvvlCbNm3k4eGhTp06JXmp+cGDB/X222/Lw8NDGTNmVKVKlXT8+HFJlpeah4SEaOPGjZo4caJ5XSdPnlTevHk1duxYi+0JDw+XyWTSsWPHkvV5AQAAAEBak+aC95UrV7Rq1Sp169ZNbm5uSfYxmUwW7wcPHqyGDRtq//79at++faL+27dvV4cOHdS9e3eFh4eratWqGjp0aIrXvnnzZrVp00Y9e/bUoUOH9PXXXys0NFTDhg2TJMXHx6t27doKCwvTvHnzdOjQIY0cOVIODg6qUKGCJkyYIA8PD50/f17nz59Xnz59zMseO3asihcvrj179mjAgAGJ1n3u3DlVrlxZTk5OWrdunXbv3q327dtbhP4EEydOVPny5fXuu++a1+Xv76/27dtr9uzZFn1nz56typUrK2/evCn8aQEAAABA6pDmLjU/duyYDMNQgQIFLNq9vb11+/ZtSVK3bt00atQo87T//e9/ateunfn9iRMnLOadOHGiatWqpb59+0qS8ufPr61bt2rFihVPrKdly5ZycHCwaDt06JD8/f0T9R0yZIj69euntm3bSpLy5MmjL774Qn379tWgQYO0Zs0a7dixQxEREcqfP7+5TwJPT0+ZTKYkLyGvVq2aevfubX5/6tQpi+lTp06Vp6enFixYoAwZMpi3Mymenp5ydHSUq6urxbpCQkI0cOBA7dixQ+XKldPdu3f1/fffJzoLniA2NlaxsbHm99HR0Un2AwAAAIDULM0F70fZsWOH4uPj1apVK4uwJ0llypR57LwRERFq2LChRVv58uWTFby//PJL1ahRw6ItZ86cSfbdu3evwsLCzGe4JSkuLk63b9/WzZs3FR4erldeeeWRgfhxnrSN4eHhqlSpkjl0P42cOXOqTp06+vbbb1WuXDn9+uuvio2NVdOmTZPsP2LECA0ZMuSp1wcAAAAAqUGaC9558+aVyWTSkSNHLNoTzgy7uLgkmudRl6SnBB8fn2RfZh0TE6MhQ4aoUaNGiaY5OzsnWXtyPWkbn2XZD+rYsaNat26tL7/8UrNnz1bz5s3l6uqaZN/+/fvrww8/NL+Pjo6Wn59fitQBAAAAAC+KNBe8s2TJojfeeENTpkzR+++/nyKhulChQtq+fbtF259//vnMy31YqVKldOTIkUcG9WLFiunvv//W0aNHkzzr7ejoqLi4uKdad7FixTRnzhzdvXs3WWe9H7Wut956S25ubpo2bZpWrFihTZs2PXIZTk5OcnJyeqp6AQAAACC1SHODq0nSV199pXv37qlMmTJauHChIiIidOTIEc2bN0+HDx9OdM/1k/To0UMrVqzQ2LFjFRkZqSlTpiTrMnPp/ijkFy5csHjduHEjyb4DBw7Ud999pyFDhujgwYOKiIjQggUL9Nlnn0mSgoODVblyZTVu3FirV6/WyZMntXz5cnMtAQEBiomJ0dq1a3X58uUnPq/8Qd27d1d0dLRatGihXbt2KTIyUnPnzk105UCCgIAAbd++XadOndLly5cVHx8vSXJwcFBISIj69++vfPnyqXz58smuAQAAAADSojQZvAMDA7Vnzx7VqFFD/fv3V/HixVWmTBlNnjxZffr00RdffGHV8l577TXNnDlTEydOVPHixbVq1SpzGH6Sdu3aKUeOHBavyZMnJ9m3Zs2a+u2337Rq1SqVLVtWr732mr788kvlypXL3Oenn35S2bJl1bJlSxUuXFh9+/Y1n3muUKGCunTpoubNmytr1qwaPXp0srcxS5YsWrdunWJiYhQcHKzSpUtr5syZjzz73adPHzk4OKhw4cLKmjWrzpw5Y57WoUMH3blzx2LAOgAAAAB4WZkMwzDsXQTSls2bN6t69eo6e/assmfPnuz5oqOj5enpqU4zlyl36Qo2rBB4sZyL2KsprWpo9+7dKlWqlL3LAQAAQDIk5JeoqCh5eHg8tm+au8cb9hMbG6tLly5p8ODBatq0qVWhGwAAAADSqjR5qTns44cfflCuXLl07do1qy5zBwAAAIC0jOCNFBMSEqK4uDjt3r1bvr6+9i4HAAAAAF4IBG8AAAAAAGyI4A0AAAAAgA0RvAEAAAAAsCGCNwAAAAAANkTwBgAAAADAhgjeAAAAAADYUHp7FwA87NLp43J0dbN3GcBzc/FkpL1LAAAAgA0RvPHCWTL0Q3uXADx3Lq6u8vb2tncZAAAAsAGCN144GzdulLu7u73LAJ4rb29v+fv727sMAAAA2ADBGy+cEiVKyMPDw95lAAAAAECKYHA1AAAAAABsiOANAAAAAIANEbwBAAAAALAhgjcAAAAAADZE8AYAAAAAwIYI3gAAAAAA2BDBGwAAAAAAGyJ4AwAAAABgQwRvAAAAAABsiOANAAAAAIANEbwBAAAAALAhgjcAAAAAADZE8AYAAAAAwIYI3gAAAAAA2BDBGwAAAAAAGyJ4AwAAAABgQwRvAAAAAABsiOANAAAAAIANEbwBAAAAALAhgjcAAAAAADZE8AYAAAAAwIYI3gAAAAAA2BDBGwAAAAAAGyJ4AwAAAABgQwRvAAAAAABsiOANAAAAAIANEbwBAAAAALCh9PYuAHhYeHi43N3d7V3GC8Xb21v+/v72LgMAAADAUyB444UTHBxs7xJeOC6urjocEUH4BgAAAFIhgjdeOA0/Gy/fQsXsXcYL4+LJSC367D1dvnyZ4A0AAACkQgRvvHCy5gqUb6Hi9i4DAAAAAFIEg6sBAAAAAGBDBG8AAAAAAGyI4A0AAAAAgA0RvAEAAAAAsCGCNwAAAAAANkTwBgAAAADAhgjeAAAAAADYEMEbAAAAAAAbInjD7MKFC+rZs6fy5s0rZ2dnZc+eXRUrVtS0adN08+ZNe5cHAAAAAKlSensXgBfDiRMnVLFiRXl5eWn48OEKCgqSk5OT9u/frxkzZsjX11f16tWzd5kAAAAAkOpwxhuSpK5duyp9+vTatWuXmjVrpkKFCilPnjyqX7++fv/9d9WtW1eSNH78eAUFBcnNzU1+fn7q2rWrYmJizMs5ffq06tatq0yZMsnNzU1FihTRH3/8Ya/NAgAAAAC744w3dOXKFa1atUrDhw+Xm5tbkn1MJpMkKV26dJo0aZJy586tEydOqGvXrurbt6+++uorSVK3bt10584dbdq0SW5ubjp06JDc3d2f27YAAAAAwIuG4A0dO3ZMhmGoQIECFu3e3t66ffu2pPuBetSoUerVq5d5ekBAgIYOHaouXbqYg/eZM2fUuHFjBQUFSZLy5MnzyPXGxsYqNjbW/D46OjqlNgkAAAAAXhhcao5H2rFjh8LDw1WkSBFzQF6zZo2qV68uX19fZcyYUa1bt9aVK1fMg6/16NFDQ4cOVcWKFTVo0CDt27fvkcsfMWKEPD09zS8/P7/nsl0AAAAA8DwRvKG8efPKZDLpyJEjFu158uRR3rx55eLiIkk6deqU3n77bRUrVkw//fSTdu/eralTp0qS7ty5I0nq2LGjTpw4odatW2v//v0qU6aMJk+enOR6+/fvr6ioKPPr7NmzNtxKAAAAALAPgjeUJUsWvfHGG5oyZYpu3LjxyH67d+9WfHy8xo0bp9dee0358+fXP//8k6ifn5+funTpop9//lm9e/fWzJkzk1yek5OTPDw8LF4AAAAAkNYQvCFJ+uqrr3Tv3j2VKVNGCxcuVEREhI4cOaJ58+bp8OHDcnBwUN68eXX37l1NnjxZJ06c0Ny5czV9+nSL5fTq1UsrV67UyZMn9ddff2n9+vUqVKiQnbYKAAAAAOyPwdUgSQoMDNSePXs0fPhw9e/fX3///becnJxUuHBh9enTR127dpWrq6vGjx+vUaNGqX///qpcubJGjBihNm3amJcTFxenbt266e+//5aHh4dq1aqlL7/80o5bBgAAAAD2ZTIMw7B3EYB0f1RzT09PdZq5TLlLV7B3OS+McxF7NaVVDe3evVulSpWydzkAAAAA9P/5JSoq6om3zXKpOQAAAAAANkTwBgAAAADAhgjeAAAAAADYEMEbAAAAAAAbIngDAAAAAGBDBG8AAAAAAGyI4A0AAAAAgA0RvAEAAAAAsCGCNwAAAAAANpT+aWY6fvy4Zs+erePHj2vixInKli2bli9fLn9/fxUpUiSla8RL5tLp43J0dbN3GS+Miycj7V0CAAAAgGdgMgzDsGaGjRs3qnbt2qpYsaI2bdqkiIgI5cmTRyNHjtSuXbu0ePFiW9WKNC46Olqenp72LuOF5OLqqsMREfL397d3KQAAAAD0//klKipKHh4ej+1r9Rnvfv36aejQofrwww+VMWNGc3u1atU0ZcoU66sFHrJx40a5u7vbu4wXire3N6EbAAAASKWsDt779+/X999/n6g9W7Zsunz5cooUhZdbiRIlnvgXIwAAAABILaweXM3Ly0vnz59P1L5nzx75+vqmSFEAAAAAAKQVVgfvFi1a6OOPP9aFCxdkMpkUHx+vsLAw9enTR23atLFFjQAAAAAApFpWB+/hw4erYMGC8vPzU0xMjAoXLqzKlSurQoUK+uyzz2xRIwAAAAAAqZbVo5onOHv2rPbv36+YmBiVLFlS+fLlS+na8JKxZlRAAAAAALAnm45qnsDPz09+fn5POzsAAAAAAC8Fqy81b9y4sUaNGpWoffTo0WratGmKFAUAAAAAQFphdfDetGmT3nrrrUTttWvX1qZNm1KkKAAAAAAA0gqrg3dMTIwcHR0TtWfIkEHR0dEpUhQAAAAAAGmF1cE7KChICxcuTNS+YMECFS5cOEWKAgAAAAAgrbB6cLUBAwaoUaNGOn78uKpVqyZJWrt2rX744Qf9+OOPKV4gAAAAAACpmdXBu27dulq6dKmGDx+uxYsXy8XFRcWKFdOaNWsUHBxsixoBAAAAAEi1rAre9+7d0/Dhw9W+fXuFhYXZqiYAAAAAANIMq+7xTp8+vUaPHq179+7Zqh4AAAAAANIUqwdXq169ujZu3GiLWgAAAAAASHOsvse7du3a6tevn/bv36/SpUvLzc3NYnq9evVSrDgAAAAAAFI7k2EYhjUzpEv36JPkJpNJcXFxz1wUXk7R0dHy9PRUVFSUPDw87F0OAAAAADySNfnF6jPe8fHxT10YAAAAAAAvG6vv8QYAAAAAAMln9Rnvzz///LHTBw4c+NTFAAAAAACQ1lgdvJcsWWLx/u7duzp58qTSp0+vwMBAgjcAAAAAAA+wOnjv2bMnUVt0dLRCQkLUsGHDFCkKAAAAAIC0IkXu8fbw8NCQIUM0YMCAlFgcAAAAAABpRooNrhYVFaWoqKiUWhwAAAAAAGmC1ZeaT5o0yeK9YRg6f/685s6dq9q1a6dYYQAAAAAApAVWB+8vv/zS4n26dOmUNWtWtW3bVv3790+xwgAAAAAASAusDt4nT560RR0AAAAAAKRJz3SP999//62///47pWoBAAAAACDNsfqMd3x8vIYOHapx48YpJiZGkpQxY0b17t1bn376qdKlS7Hx2vCSCg8Pl7u7u73LsBlvb2/5+/vbuwwAAAAAz4nVwfvTTz/VN998o5EjR6pixYqSpC1btmjw4MG6ffu2hg0bluJF4uUSHBxs7xJsysXVVYcjIgjfAAAAwEvCZBiGYc0MOXPm1PTp01WvXj2L9mXLlqlr1646d+5cihaIl0d0dLQ8PT3V8LPx8i1UzN7l2MTFk5Fa9Nl72r17t0qVKmXvcgAAAAA8pYT8EhUVJQ8Pj8f2tfqM99WrV1WwYMFE7QULFtTVq1etXRyQSNZcgfItVNzeZQAAAABAirD6huzixYtrypQpidqnTJmi4sUJSwAAAAAAPMjqM96jR49WnTp1tGbNGpUvX16StG3bNp09e1Z//PFHihcIAAAAAEBqZvUZ7+DgYB09elQNGzbUtWvXdO3aNTVq1EhHjhxRpUqVbFEjAAAAAACpltVnvKX7A6wxejkAAAAAAE+W7DPely9f1unTpy3aDh48qHbt2qlZs2b6/vvvU7w4AAAAAABSu2QH7/fff1+TJk0yv7948aIqVaqknTt3KjY2ViEhIZo7d65NigQAAAAAILVKdvD+888/LZ7d/d133ylz5swKDw/XsmXLNHz4cE2dOtUmRQIAAAAAkFolO3hfuHBBAQEB5vfr1q1To0aNlD79/dvE69Wrp8jIyBQvEAAAAACA1CzZwdvDw0PXrl0zv9+xY4deffVV83uTyaTY2NgULe5lVKVKFfXq1cveZaSIwYMHq0SJEvYuAwAAAADsKtnB+7XXXtOkSZMUHx+vxYsX6/r166pWrZp5+tGjR+Xn52eTItOikJAQmUymRK/Ro0friy++sHd5AAAAAIAUkuzHiX3xxReqXr265s2bp3v37umTTz5RpkyZzNMXLFig4OBgmxSZVtWqVUuzZ8+2aMuaNascHBweOc+dO3fk6Oho69IAAAAAACkk2We8ixUrpoiICC1atEhbt25NdFa2RYsW+vjjj1O8wLTMyclJPj4+Fq/q1atbXGoeEBCgL774Qm3atJGHh4c6deokSdqyZYsqVaokFxcX+fn5qUePHrpx44YkacqUKSpatKh5GUuXLpXJZNL06dPNbTVq1NBnn31mfj9t2jQFBgbK0dFRBQoUSDRC/ZkzZ1S/fn25u7vLw8NDzZo107///mvRZ+TIkcqePbsyZsyoDh066Pbt2yn2WQEAAABAapXs4C1J3t7eql+/vsW93Qnq1Kmj3Llzp1hh+H9jx45V8eLFtWfPHg0YMEDHjx9XrVq11LhxY+3bt08LFy7Uli1b1L17d0lScHCwDh06pEuXLkmSNm7cKG9vb23YsEGSdPfuXW3btk1VqlSRJC1ZskQ9e/ZU7969deDAAXXu3Fnt2rXT+vXrJUnx8fGqX7++rl69qo0bN2r16tU6ceKEmjdvbq5x0aJFGjx4sIYPH65du3YpR44c+uqrrx67XbGxsYqOjrZ4AQAAAEBaYzIMw7B3ES+jkJAQzZs3T87Ozua22rVr69KlSypRooQmTJgg6f4Z75IlS2rJkiXmfh07dpSDg4O+/vprc9uWLVsUHBysGzduyMnJSVmzZtX06dPVpEkTlSxZUs2bN9fEiRN1/vx5hYWFqWrVqrp27ZpcXV1VsWJFFSlSRDNmzDAvr1mzZrpx44Z+//13rV69WrVr19bJkyfN9/EfOnRIRYoU0Y4dO1S2bFlVqFBBJUuWtHik3Guvvabbt28rPDw8yc9g8ODBGjJkSKL2TjOXKXfpCk/1ub7ozkXs1ZRWNbR7926VKlXK3uUAAAAAeErR0dHy9PRUVFSUPDw8HtvXqjPeSFlVq1ZVeHi4+TVp0qQk+5UpU8bi/d69exUaGip3d3fzq2bNmoqPj9fJkydlMplUuXJlbdiwQdeuXdOhQ4fUtWtXxcbG6vDhw9q4caPKli0rV1dXSVJERIQqVqxosY6KFSsqIiLCPN3Pz89i8LzChQvLy8vLos/DV0KUL1/+sdvfv39/RUVFmV9nz55NxqcGAAAAAKlLsgdXQ8pzc3NT3rx5k9XvQTExMercubN69OiRqK+/v7+k+48lmzFjhjZv3qySJUvKw8PDHMY3btz4QgyE5+TkJCcnJ3uXAQAAAAA2xRnvVKhUqVI6dOiQ8ubNm+iVMOJ5wn3eP/74o/le7ipVqmjNmjUKCwszt0lSoUKFFBYWZrGOsLAwFS5c2Dz97NmzFmekDx06pGvXrln02b59u8Uy/vzzz5TedAAAAABIdawO3sHBwfruu+9069YtW9SDZPj444+1detWde/eXeHh4YqMjNSyZcvMg6tJ90ehz5Qpk77//nuL4L106VLFxsZaXFr+0UcfKTQ0VNOmTVNkZKTGjx+vn3/+WX369JF0fwT0oKAgtWrVSn/99Zd27NihNm3aKDg42HwZfM+ePfXtt99q9uzZOnr0qAYNGqSDBw8+vw8FAAAAAF5QVgfvkiVLqk+fPvLx8dG7777LWU07KFasmDZu3KijR4+qUqVKKlmypAYOHKicOXOa+5hMJlWqVEkmk0mvv/66eT4PDw+VKVPG4vL1Bg0aaOLEiRo7dqyKFCmir7/+WrNnzzYHdpPJpGXLlilTpkyqXLmyatSooTx58mjhwoXmZTRv3lwDBgxQ3759Vbp0aZ0+fVrvvffe8/lAAAAAAOAF9lSjmt+7d0+//PKL5syZo+XLlytv3rxq3769WrdurezZs9uiTrwEEkYFZFRzAAAAAC86m49qnj59ejVq1EjLli3T33//rf/9738aMGCA/Pz81KBBA61bt+6pCgcAAAAAIK15psHVduzYoUGDBmncuHHKli2b+vfvL29vb7399tvm+4MBAAAAAHiZWf04sYsXL2ru3LmaPXu2IiMjVbduXf3www+qWbOmTCaTJCkkJES1atXS2LFjU7xgAAAAAABSE6uD9yuvvKLAwEC1b99eISEhypo1a6I+xYoVU9myZVOkQAAAAAAAUjOrg/fatWtVqVKlx/bx8PDQ+vXrn7ooAAAAAADSCqvv8X5S6AYAAAAAAP/P6jPeJUuWNN/L/SCTySRnZ2flzZtXISEhqlq1aooUCAAAAABAamb1Ge9atWrpxIkTcnNzU9WqVVW1alW5u7vr+PHjKlu2rM6fP68aNWpo2bJltqgXAAAAAIBUxeoz3pcvX1bv3r01YMAAi/ahQ4fq9OnTWrVqlQYNGqQvvvhC9evXT7FC8fK4dPq4HF3d7F2GTVw8GWnvEgAAAAA8ZybDMAxrZvD09NTu3buVN29ei/Zjx46pdOnSioqK0uHDh1W2bFldv349RYtF2hYdHS1PT097l2FzLq6uOhwRIX9/f3uXAgAAAOApJeSXqKgoeXh4PLav1We8nZ2dtXXr1kTBe+vWrXJ2dpYkxcfHm/8NWGvjxo1yd3e3dxk24+3tTegGAAAAXiJWB+/3339fXbp00e7du83P6t65c6dmzZqlTz75RJK0cuVKlShRIkULxcujRIkST/yLEQAAAACkFlZfai5J8+fP15QpU3TkyBFJUoECBfT+++/rf//7nyTp1q1b5lHOgeSy5lINAAAAALAnm11qfu/ePQ0fPlzt27dXq1atHtnPxcXFmsUCAAAAAJBmWfU4sfTp02v06NG6d++ereoBAAAAACBNsfo53tWrV9fGjRttUQsAAAAAAGmO1YOr1a5dW/369dP+/ftVunRpublZPm+5Xr16KVYcAAAAAACpndWDq6VL9+iT5CaTSXFxcc9cFF5ODK4GAAAAILWw6XO84+Pjn7owAAAAAABeNlbf4/2g27dvp1QdAAAAAACkSVYH77i4OH3xxRfy9fWVu7u7Tpw4IUkaMGCAvvnmmxQvEAAAAACA1Mzq4D1s2DCFhoZq9OjRcnR0NLcXLVpUs2bNStHiAAAAAABI7awO3t99951mzJihVq1aycHBwdxevHhxHT58OEWLAwAAAAAgtbM6eJ87d0558+ZN1B4fH6+7d++mSFEAAAAAAKQVVgfvwoULa/PmzYnaFy9erJIlS6ZIUQAAAAAApBVWP05s4MCBatu2rc6dO6f4+Hj9/PPPOnLkiL777jv99ttvtqgRAAAAAIBUy+oz3vXr19evv/6qNWvWyM3NTQMHDlRERIR+/fVXvfHGG7aoEQAAAACAVMtkGIZh7yIASYqOjpanp6eioqLk4eFh73IAAAAA4JGsyS9WX2qe4M6dO7p48aLi4+Mt2v39/Z92kQAAAAAApDlWB+/IyEi1b99eW7dutWg3DEMmk0lxcXEpVhwAAAAAAKmd1cE7JCRE6dOn12+//aYcOXLIZDLZoi4AAAAAANIEq4N3eHi4du/erYIFC9qiHgAAAAAA0pSneo735cuXbVELAAAAAABpjtXBe9SoUerbt682bNigK1euKDo62uIFAAAAAAD+n9WPE0uX7n5Wf/jebgZXw7PicWIAAAAAUgubPk5s/fr1T10YAAAAAAAvG6uDd3BwsC3qAAAAAAAgTUr2Pd6jR4/WrVu3zO/DwsIUGxtrfn/9+nV17do1ZasDAAAAACCVS/Y93g4ODjp//ryyZcsmSfLw8FB4eLjy5MkjSfr333+VM2dO7vHGU+MebwAAAACphU3u8X44n1s5JhuQbOHh4XJ3d3+mZXh7e8vf3z+FKgIAAACAp2f1Pd6AraXEOAIurq46HBFB+AYAAABgdwRvvHAafjZevoWKPfX8F09GatFn7+ny5csEbwAAAAB2Z1XwnjVrlvkS4Hv37ik0NFTe3t6S7g+uBqSErLkC5VuouL3LAAAAAIAUkezg7e/vr5kzZ5rf+/j4aO7cuYn6AAAAAACA/5fs4H3q1CkblgEAAAAAQNqU7Od4AwAAAAAA6xG8AQAAAACwIYI3AAAAAAA2RPAGAAAAAMCGCN4AAAAAANiQVc/xlqTo6Ogk200mk5ycnOTo6PjMRQEAAAAAkFZYfcbby8tLmTJlSvTy8vKSi4uLcuXKpUGDBik+Pt4W9aZpoaGh8vLysncZAAAAAIAUZHXwDg0NVc6cOfXJJ59o6dKlWrp0qT755BP5+vpq2rRp6tSpkyZNmqSRI0c+cVkhISEymUyJXrVq1XqqjXkRPLxNWbJkUa1atbRv374nztu8eXMdPXr0OVRp6VGBPyAgQBMmTHju9QAAAABAWmL1peZz5szRuHHj1KxZM3Nb3bp1FRQUpK+//lpr166Vv7+/hg0bpk8++eSJy6tVq5Zmz55t0ebk5GRtWVa5c+eOTS+Jf3CbLly4oM8++0xvv/22zpw588h57t69KxcXF7m4uNisLgAAAADA82f1Ge+tW7eqZMmSidpLliypbdu2SZJef/31x4bMBzk5OcnHx8filSlTJvN0k8mkWbNmqWHDhnJ1dVW+fPn0yy+/WCzjwIEDql27ttzd3ZU9e3a1bt1aly9fNk+vUqWKunfvrl69esnb21s1a9aUJP3yyy/Kly+fnJ2dVbVqVc2ZM0cmk0nXrl3TjRs35OHhocWLF1usa+nSpXJzc9P169eTtU0lSpRQv379dPbsWV26dEmSdOrUKZlMJi1cuFDBwcFydnbW/PnzkzzzPHLkSGXPnl0ZM2ZUhw4d1K9fP5UoUcJi23r16mUxT4MGDRQSEmJ+Hxsbqz59+sjX11dubm569dVXtWHDBknShg0b1K5dO0VFRZnP0g8ePFhVqlTR6dOn9cEHH5jbE2zZskWVKlWSi4uL/Pz81KNHD924ccM8/auvvjJ/rtmzZ1eTJk0e+VkBAAAAQFpndfD28/PTN998k6j9m2++kZ+fnyTpypUrFuH5WQ0ZMkTNmjXTvn379NZbb6lVq1a6evWqJOnatWuqVq2aSpYsqV27dmnFihX6999/Lc7IS/fP1Ds6OiosLEzTp0/XyZMn1aRJEzVo0EB79+5V586d9emnn5r7u7m5qUWLFonOxs+ePVtNmjRRxowZk1V7TEyM5s2bp7x58ypLliwW0/r166eePXsqIiLC/MeABy1atEiDBw/W8OHDtWvXLuXIkUNfffVVstb7oO7du2vbtm1asGCB9u3bp6ZNm6pWrVqKjIxUhQoVNGHCBHl4eOj8+fM6f/68+vTpo59//lmvvPKKPv/8c3O7JB0/fly1atVS48aNtW/fPi1cuFBbtmxR9+7dJUm7du1Sjx499Pnnn+vIkSNasWKFKleunGRdsbGxio6OtngBAAAAQFpj9aXmY8eOVdOmTbV8+XKVLVtW0v2wdfjwYfPZ4Z07d6p58+bJWt5vv/0md3d3i7ZPPvnE4jL1kJAQtWzZUpI0fPhwTZo0STt27FCtWrU0ZcoUlSxZUsOHDzf3//bbb+Xn56ejR48qf/78kqR8+fJp9OjR5j79+vVTgQIFNGbMGElSgQIFdODAAQ0bNszcp2PHjqpQoYLOnz+vHDly6OLFi/rjjz+0Zs2aZG/TjRs3lCNHDv32229Kl87y7xy9evVSo0aNHrmcCRMmqEOHDurQoYMkaejQoVqzZo1u37792PU/6MyZM5o9e7bOnDmjnDlzSpL69OmjFStWaPbs2Ro+fLg8PT1lMpnk4+NjMa+Dg4MyZsxo0T5ixAi1atXKfJY9X758mjRpkoKDgzVt2jSdOXNGbm5uevvtt5UxY0blypUrySskEpY1ZMiQZG8LAAAAAKRGVgfvevXq6fDhw/r666/NA4HVrl1bS5cuVUBAgCTpvffeS/byqlatqmnTplm0Zc6c2eJ9sWLFzP92c3OTh4eHLl68KEnau3ev1q9fnyi8S/fPziYE79KlS1tMO3LkiPkPBwnKlSuX6H2RIkU0Z84c9evXT/PmzVOuXLkeeQY3qW3677//9NVXX6l27drasWOHcuXKZe5XpkyZxy4nIiJCXbp0sWgrX7681q9f/9j5HrR//37FxcWZP4cEsbGxic7AJ8fevXu1b98+zZ8/39xmGIbi4+N18uRJvfHGG8qVK5fy5MmjWrVqqVatWubbBB7Wv39/ffjhh+b30dHR5qsmAAAAACCtsDp4S1Lu3LmTNWp5cri5uSlv3ryP7ZMhQwaL9yaTyfy4spiYGNWtW1ejRo1KNF+OHDks1vM0OnbsqKlTp6pfv36aPXu22rVrZ3G/c1Ie3qZZs2bJ09NTM2fO1NChQ5+5pgelS5dOhmFYtN29e9f875iYGDk4OGj37t1ycHCw6JfUHyueJCYmRp07d1aPHj0STfP395ejo6P++usvbdiwQatWrdLAgQM1ePBg7dy5M9H9605OTjYfSA8AAAAA7O2pgve1a9e0Y8cOXbx4MdHzutu0aZMihSVXqVKl9NNPPykgIEDp0yd/cwoUKKA//vjDom3nzp2J+r3zzjvq27evJk2apEOHDqlt27ZW12gymZQuXTrdunXLqvkKFSqk7du3W3ymf/75p0WfrFmzmu+/lqS4uDgdOHBAVatWlXR/0Lu4uDhdvHhRlSpVSnI9jo6OiouLS1Z7qVKldOjQocf+sSR9+vSqUaOGatSooUGDBsnLy0vr1q177GX1AAAAAJBWWR28f/31V7Vq1UoxMTHy8PCwOPtrMpmsDt6xsbG6cOGCZVHp08vb2ztZ83fr1k0zZ85Uy5Yt1bdvX2XOnFnHjh3TggULNGvWrERneRN07txZ48eP18cff6wOHTooPDxcoaGh5u1IkClTJjVq1EgfffSR3nzzTb3yyitWbdN///2nKVOmmM/MW6Nnz54KCQlRmTJlVLFiRc2fP18HDx5Unjx5zH2qVaumDz/8UL///rsCAwM1fvx4Xbt2zTw9f/78atWqldq0aaNx48apZMmSunTpktauXatixYqpTp06CggIUExMjNauXavixYvL1dVVrq6uCggI0KZNm9SiRQs5OTnJ29tbH3/8sV577TV1795dHTt2lJubmw4dOqTVq1drypQp+u2333TixAlVrlxZmTJl0h9//KH4+HgVKFDAqm0HAAAAgLTC6lHNe/furfbt2ysmJkbXrl3Tf//9Z34ljDRujRUrVihHjhwWr9dffz3Z8+fMmVNhYWGKi4vTm2++qaCgIPXq1UteXl6JBjN7UO7cubV48WL9/PPPKlasmKZNm2Ye1fzhy587dOigO3fuqH379lZv06uvvqqdO3fqxx9/VJUqVZK9XZLUvHlzDRgwQH379lXp0qV1+vTpRPfPt2/fXm3btlWbNm0UHBysPHnymM92J5g9e7batGmj3r17q0CBAmrQoIF27twpf39/SVKFChXUpUsXNW/eXFmzZjUPQvf555/r1KlTCgwMVNasWSXdv99+48aNOnr0qCpVqqSSJUtq4MCB5oHbvLy89PPPP6tatWoqVKiQpk+frh9++EFFihSxatsBAAAAIK0wGQ/fIPwEbm5u2r9/v8VZ17Ri2LBhmj59us6ePWvRPnfuXH3wwQf6559/5OjoaKfq7hs8eLCWLl2q8PBwu9ZhC9HR0fL09FSnmcuUu3SFp17OuYi9mtKqhnbv3q1SpUqlYIUAAAAAcF9CfomKipKHh8dj+1p9qXnNmjW1a9euNBG8v/rqK5UtW1ZZsmRRWFiYxowZY34etSTdvHlT58+f18iRI9W5c2e7h24AAAAAQOpjdfCuU6eOPvroIx06dEhBQUGJRhyvV69eihVna5GRkRo6dKiuXr0qf39/9e7dW/379zdPHz16tIYNG6bKlStbtAMAAAAAkFxWX2r+uPumTSZTkqNjA8nBpeYAAAAAUgubXmr+8OPDAAAAAADAo1k9qjkAAAAAAEi+ZJ3xnjRpkjp16iRnZ2dNmjTpsX179OiRIoUBAAAAAJAWJCt4f/nll2rVqpWcnZ315ZdfPrKfyWQieAMAAAAA8IBkBe+TJ08m+W8AAAAAAPB4Vg+uduDAARUtWjTJaUuXLlWDBg2etSa85C6dPi5HV7ennv/iycgUrAYAAAAAno3VjxPz9fXVli1blDt3bov2n376SW3atNGNGzdStEC8PBKG408JLq6uOhwRIX9//xRZHgAAAAA8yKaPE+vYsaNq1KihsLAw+fj4SJIWLlyo9u3bKzQ09KkKBh60ceNGubu7P9MyvL29Cd0AAAAAXghWB+8hQ4bo6tWrqlGjhjZt2qQVK1aoY8eOmjt3rho3bmyLGvGSKVGixBP/YgQAAAAAqYXVwVuSJk+erFatWum1117TuXPn9MMPP6h+/fopXRsAAAAAAKlesoL3L7/8kqitUaNG2rx5s1q2bCmTyWTuU69evZStEAAAAACAVCxZg6ulS5cueQszmRQXF/fMReHlZM3gBAAAAABgTyk+uFp8fHyKFAYAAAAAwMsmeaeyAQAAAADAU3mq4L1x40bVrVtXefPmVd68eVWvXj1t3rw5pWsDAAAAACDVszp4z5s3TzVq1JCrq6t69OihHj16yMXFRdWrV9f3339vixoBAAAAAEi1kjW42oMKFSqkTp066YMPPrBoHz9+vGbOnKmIiIgULRAvDwZXAwAAAJBaWJNfrD7jfeLECdWtWzdRe7169XTy5ElrFwcAAAAAQJpmdfD28/PT2rVrE7WvWbNGfn5+KVIUAAAAAABpRbIeJ/ag3r17q0ePHgoPD1eFChUkSWFhYQoNDdXEiRNTvEAAAAAAAFIzq4P3e++9Jx8fH40bN06LFi2SdP++74ULF6p+/fopXiAAAAAAAKmZ1YOrAbbC4GoAAAAAUgtr8ovVZ7wT7N692zyCeZEiRVSyZMmnXRQAAAAAAGmW1cH74sWLatGihTZs2CAvLy9J0rVr11S1alUtWLBAWbNmTekaAQAAAABItawe1fz999/X9evXdfDgQV29elVXr17VgQMHFB0drR49etiiRgAAAAAAUi2r7/H29PTUmjVrVLZsWYv2HTt26M0339S1a9dSsj68RLjHGwAAAEBqYU1+sfqMd3x8vDJkyJCoPUOGDIqPj7d2cQAAAAAApGlWB+9q1aqpZ8+e+ueff8xt586d0wcffKDq1aunaHEAAAAAAKR2VgfvKVOmKDo6WgEBAQoMDFRgYKBy586t6OhoTZ482RY1AgAAAACQalk9qrmfn5/++usvrVmzRocPH5YkFSpUSDVq1Ejx4gAAAAAASO2sHlwNsBUGVwMAAACQWthkcLV169apcOHCio6OTjQtKipKRYoU0ebNm62vFgAAAACANCzZwXvChAl69913k0zynp6e6ty5s8aPH5+ixQEAAAAAkNolO3jv3btXtWrVeuT0N998U7t3706RogAAAAAASCuSHbz//fffJJ/fnSB9+vS6dOlSihQFAAAAAEBakezg7evrqwMHDjxy+r59+5QjR44UKQoAAAAAgLQi2Y8Te+uttzRgwADVqlVLzs7OFtNu3bqlQYMG6e23307xAvHyCQ8Pl7u7+1PN6+3tLX9//xSuCAAAAACeXrIfJ/bvv/+qVKlScnBwUPfu3VWgQAFJ0uHDhzV16lTFxcXpr7/+Uvbs2W1aMNKuhOH4n4WLq6sOR0QQvgEAAADYlDWPE0v2Ge/s2bNr69ateu+999S/f38l5HWTyaSaNWtq6tSphG6kiIafjZdvoWJWz3fxZKQWffaeLl++TPAGAAAA8MJIdvCWpFy5cumPP/7Qf//9p2PHjskwDOXLl0+ZMmWyVX14CWXNFSjfQsXtXQYAAAAApAirgneCTJkyqWzZsildCwAAAAAAaU6yRzUHAAAAAADWI3gDAAAAAGBDBG8AAAAAAGyI4A0AAAAAgA0RvAEAAAAAsCGCNwAAAAAANkTwBgAAAADAhgjeAAAAAADYEME7jQsJCZHJZNLIkSMt2pcuXSqTyWSnqgAAAADg5UHwfgk4Oztr1KhR+u+//+xdCgAAAAC8dAjeL4EaNWrIx8dHI0aMeGSfn376SUWKFJGTk5MCAgI0btw4i+kBAQEaPny42rdvr4wZM8rf318zZsyw6HP27Fk1a9ZMXl5eypw5s+rXr69Tp07ZYpMAAAAAINUgeL8EHBwcNHz4cE2ePFl///13oum7d+9Ws2bN1KJFC+3fv1+DBw/WgAEDFBoaatFv3LhxKlOmjPbs2aOuXbvqvffe05EjRyRJd+/eVc2aNZUxY0Zt3rxZYWFhcnd3V61atXTnzp0k64qNjVV0dLTFCwAAAADSGoL3S6Jhw4YqUaKEBg0alGja+PHjVb16dQ0YMED58+dXSEiIunfvrjFjxlj0e+utt9S1a1flzZtXH3/8sby9vbV+/XpJ0sKFCxUfH69Zs2YpKChIhQoV0uzZs3XmzBlt2LAhyZpGjBghT09P88vPzy/FtxsAAAAA7I3g/RIZNWqU5syZo4iICIv2iIgIVaxY0aKtYsWKioyMVFxcnLmtWLFi5n+bTCb5+Pjo4sWLkqS9e/fq2LFjypgxo9zd3eXu7q7MmTPr9u3bOn78eJL19O/fX1FRUebX2bNnU2pTAQAAAOCFkd7eBeD5qVy5smrWrKn+/fsrJCTE6vkzZMhg8d5kMik+Pl6SFBMTo9KlS2v+/PmJ5suaNWuSy3NycpKTk5PVdQAAAABAakLwfsmMHDlSJUqUUIECBcxthQoVUlhYmEW/sLAw5c+fXw4ODslabqlSpbRw4UJly5ZNHh4eKVozAAAAAKRmXGr+kgkKClKrVq00adIkc1vv3r21du1affHFFzp69KjmzJmjKVOmqE+fPslebqtWreTt7a369etr8+bNOnnypDZs2KAePXokOaAbAAAAALwsCN4voc8//9x8ibh0/2z1okWLtGDBAhUtWlQDBw7U559/btXl6K6urtq0aZP8/f3VqFEjFSpUSB06dNDt27c5Aw4AAADgpWYyDMOwdxGAJEVHR8vT01OdZi5T7tIVrJ7/XMReTWlVQ7t371apUqVsUCEAAAAA3JeQX6Kiop54spEz3gAAAAAA2BDBGwAAAAAAGyJ4AwAAAABgQwRvAAAAAABsiOANAAAAAIANEbwBAAAAALAhgjcAAAAAADZE8AYAAAAAwIbS27sA4GGXTh+Xo6ub1fNdPBlpg2oAAAAA4NkQvPHCWTL0w6ee18XVVd7e3ilYDQAAAAA8G4I3XjgbN26Uu7v7U83r7e0tf3//FK4IAAAAAJ4ewRsvnBIlSsjDw8PeZQAAAABAimBwNQAAAAAAbIjgDQAAAACADRG8AQAAAACwIYI3AAAAAAA2RPAGAAAAAMCGCN4AAAAAANgQwRsAAAAAABsieAMAAAAAYEMEbwAAAAAAbIjgDQAAAACADRG8AQAAAACwIYI3AAAAAAA2RPAGAAAAAMCGCN4AAAAAANgQwRsAAAAAABsieAMAAAAAYEMEbwAAAAAAbIjgDQAAAACADRG8AQAAAACwIYI3AAAAAAA2RPAGAAAAAMCGCN4AAAAAANgQwRsAAAAAABsieAMAAAAAYEMEbwAAAAAAbIjgDQAAAACADRG8AQAAAACwofT2LgB4WHh4uNzd3Z/Yz9vbW/7+/s+hIgAAAAB4egRvvHCCg4OT1c/F1VWHIyII3wAAAABeaARvvHAafjZevoWKPbbPxZORWvTZe7p8+TLBGwAAAMALjeCNF07WXIHyLVTc3mUAAAAAQIpgcDUAAAAAAGyI4A0AAAAAgA0RvAEAAAAAsCGCNwAAAAAANkTwBgAAAADAhgjeAAAAAADYEMEbAAAAAAAbIngDAAAAAGBDBG9YCA0NlZeXl73LAAAAAIA0g+CdRp09e1bt27dXzpw55ejoqFy5cqlnz566cuWKuU9AQIAmTJhgvyIBAAAA4CVA8E6DTpw4oTJlyigyMlI//PCDjh07punTp2vt2rUqX768rl69+txrunv37nNfJwAAAAC8CAjeaVC3bt3k6OioVatWKTg4WP7+/qpdu7bWrFmjc+fO6dNPP1WVKlV0+vRpffDBBzKZTDKZTBbLWLlypQoVKiR3d3fVqlVL58+ft5g+a9YsFSpUSM7OzipYsKC++uor87RTp07JZDJp4cKFCg4OlrOzs+bPn/9cth0AAAAAXjTp7V0AUtbVq1e1cuVKDRs2TC4uLhbTfHx81KpVKy1cuFCRkZEqUaKEOnXqpHfffdei382bNzV27FjNnTtX6dKl0zvvvKM+ffqYw/P8+fM1cOBATZkyRSVLltSePXv07rvvys3NTW3btjUvp1+/fho3bpxKliwpZ2fnRLXGxsYqNjbW/D46OjolPwoAAAAAeCEQvNOYyMhIGYahQoUKJTm9UKFC+u+//xQXFycHBwdlzJhRPj4+Fn3u3r2r6dOnKzAwUJLUvXt3ff755+bpgwYN0rhx49SoUSNJUu7cuXXo0CF9/fXXFsG7V69e5j5JGTFihIYMGfLU2woAAAAAqQGXmqdRhmE89byurq7m0C1JOXLk0MWLFyVJN27c0PHjx9WhQwe5u7ubX0OHDtXx48ctllOmTJnHrqd///6Kiooyv86ePfvUNQMAAADAi4oz3mlM3rx5ZTKZFBERoYYNGyaaHhERoUyZMilr1qyPXEaGDBks3ptMJnOQj4mJkSTNnDlTr776qkU/BwcHi/dubm6PrdXJyUlOTk6P7QMAAAAAqR1nvNOYLFmy6I033tBXX32lW7duWUy7cOGC5s+fr+bNm8tkMsnR0VFxcXFWLT979uzKmTOnTpw4obx581q8cufOnZKbAgAAAABpAsE7DZoyZYpiY2NVs2ZNbdq0SWfPntWKFSv0xhtvyNfXV8OGDZN0/znemzZt0rlz53T58uVkL3/IkCEaMWKEJk2apKNHj2r//v2aPXu2xo8fb6tNAgAAAIBUi+CdBuXLl0+7du1Snjx51KxZMwUGBqpTp06qWrWqtm3bpsyZM0uSPv/8c506dUqBgYGPvfT8YR07dtSsWbM0e/ZsBQUFKTg4WKGhoZzxBgAAAIAkmIxnGYULSEHR0dHy9PRUp5nLlLt0hcf2PRexV1Na1dDu3btVqlSp51QhAAAAANyXkF+ioqLk4eHx2L6c8QYAAAAAwIYI3gAAAAAA2BDBGwAAAAAAGyJ4AwAAAABgQwRvAAAAAABsiOANAAAAAIANEbwBAAAAALAhgjcAAAAAADaU3t4FAA+7dPq4HF3dHtvn4snI51QNAAAAADwbgjdeOEuGfpisfi6urvL29rZxNQAAAADwbAjeeOFs3LhR7u7uT+zn7e0tf3//51ARAAAAADw9gjdeOCVKlJCHh4e9ywAAAACAFMHgagAAAAAA2BDBGwAAAAAAGyJ4AwAAAABgQwRvAAAAAABsiOANAAAAAIANEbwBAAAAALAhgjcAAAAAADZE8AYAAAAAwIYI3gAAAAAA2BDBGwAAAAAAGyJ4AwAAAABgQwRvAAAAAABsiOANAAAAAIANEbwBAAAAALAhgjcAAAAAADZE8AYAAAAAwIYI3gAAAAAA2BDBGwAAAAAAGyJ4AwAAAABgQwRvAAAAAABsiOANAAAAAIANEbwBAAAAALAhgjcAAAAAADZE8AYAAAAAwIYI3gAAAAAA2BDBGwAAAAAAGyJ4AwAAAABgQwRvAAAAAABsiOCNF86FCxfsXQIAAAAApBiCN144BG8AAAAAaQnBGwAAAAAAGyJ4AwAAAABgQwRvAAAAAABsiOANAAAAAIANEbwBAAAAALAhgjcAAAAAADZE8AYAAAAAwIYI3gAAAAAA2BDBGwAAAAAAGyJ4P6XBgwerRIkSaWY9AAAAAADbeGmD97Zt2+Tg4KA6deo81fx9+vTR2rVrU7gq6+XIkUMjR460aOvXr59MJpM2bNhg0V6lShW1bt36mdcZEhKiBg0aPPNyAAAAAOBl8NIG72+++Ubvv/++Nm3apH/++cfq+d3d3ZUlSxYbVGadKlWqJArY69evl5+fn0X77du39eeff6patWrPt0AAAAAAeMm9lME7JiZGCxcu1Hvvvac6deooNDTUYvqGDRtkMpm0du1alSlTRq6urqpQoYKOHDli7vPwJeAJZ4GHDx+u7Nmzy8vLS59//rnu3bunjz76SJkzZ9Yrr7yi2bNnW6zr448/Vv78+eXq6qo8efJowIABunv3brK3pWrVqgoLC9O9e/ckSdevX9eePXv08ccfWwTvbdu2KTY2VlWrVtWVK1fUsmVL+fr6ytXVVUFBQfrhhx8slrt48WIFBQXJxcVFWbJkUY0aNXTjxg0NHjxYc+bM0bJly2QymSzOrJ89e1bNmjWTl5eXMmfOrPr16+vUqVPJ3hYAAAAASIteyuC9aNEiFSxYUAUKFNA777yjb7/9VoZhJOr36aefaty4cdq1a5fSp0+v9u3bP3a569at0z///KNNmzZp/PjxGjRokN5++21lypRJ27dvV5cuXdS5c2f9/fff5nkyZsyo0NBQHTp0SBMnTtTMmTP15ZdfJntbqlatqpiYGO3cuVOStHnzZuXPn1+NGzfW9u3bdfv2bUn3z4IHBAQoICBAt2/fVunSpfX777/rwIED6tSpk1q3bq0dO3ZIks6fP6+WLVuqffv2ioiI0IYNG9SoUSMZhqE+ffqoWbNmqlWrls6fP6/z58+rQoUKunv3rmrWrKmMGTNq8+bNCgsLk7u7u2rVqqU7d+4kWXtsbKyio6MtXgAAAACQ1ryUwfubb77RO++8I0mqVauWoqKitHHjxkT9hg0bpuDgYBUuXFj9+vXT1q1bzUE2KZkzZ9akSZNUoEABtW/fXgUKFNDNmzf1ySefKF++fOrfv78cHR21ZcsW8zyfffaZKlSooICAANWtW1d9+vTRokWLkr0t+fLlk6+vr/ms84YNGxQcHCwfHx/5+/tr27Zt5vaqVatKknx9fdWnTx+VKFFCefLk0fvvv69atWqZ13v+/Hndu3dPjRo1UkBAgIKCgtS1a1e5u7vL3d1dLi4ucnJyko+Pj3x8fOTo6KiFCxcqPj5es2bNUlBQkAoVKqTZs2frzJkziS6FTzBixAh5enqaX35+fsnebgAAAABILV664H3kyBHt2LFDLVu2lCSlT59ezZs31zfffJOob7Fixcz/zpEjhyTp4sWLj1x2kSJFlC7d/3+k2bNnV1BQkPm9g4ODsmTJYrGMhQsXqmLFivLx8ZG7u7s+++wznTlzxqptevA+7w0bNqhKlSqSpODgYG3YsEG3bt3S9u3bzcE7Li5OX3zxhYKCgpQ5c2a5u7tr5cqV5vUWL15c1atXV1BQkJo2baqZM2fqv//+e2wNe/fu1bFjx5QxY0ZzQM+cObNu376t48ePJzlP//79FRUVZX6dPXvWqu0GAAAAgNQgvb0LeN6++eYb3bt3Tzlz5jS3GYYhJycnTZkyRZ6enub2DBkymP9tMpkkSfHx8Y9c9oP9E+ZJqi1hGdu2bVOrVq00ZMgQ1axZU56enlqwYIHGjRtn1TZVrVpVPXv21JUrV7Rnzx4FBwdLuh+8v/76a1WuXFl37twxD6w2ZswYTZw4URMmTFBQUJDc3NzUq1cv8yXhDg4OWr16tbZu3apVq1Zp8uTJ+vTTT7V9+3blzp07yRpiYmJUunRpzZ8/P9G0rFmzJjmPk5OTnJycrNpWAAAAAEhtXqoz3vfu3dN3332ncePGKTw83Pzau3evcubMmWiAMVvbunWrcuXKpU8//VRlypRRvnz5dPr0aauXU7VqVd24cUPjx49Xvnz5lC1bNklS5cqVtWPHDi1fvtx8SbokhYWFqX79+nrnnXdUvHhx5cmTR0ePHrVYpslkUsWKFTVkyBDt2bNHjo6OWrJkiSTJ0dFRcXFxFv1LlSqlyMhIZcuWTXnz5rV4PfjHDAAAAAB42bxUwfu3337Tf//9pw4dOqho0aIWr8aNGyd5ubkt5cuXT2fOnNGCBQt0/PhxTZo0yRxurZEnTx75+/tr8uTJ5rPdkuTn56ecOXNqxowZ5svME9abcEY7IiJCnTt31r///muevn37dg0fPly7du3SmTNn9PPPP+vSpUsqVKiQJCkgIED79u3TkSNHdPnyZd29e1etWrWSt7e36tevr82bN+vkyZPasGGDevToYTGYHAAAAAC8bF6q4P3NN9+oRo0aSZ6Bbdy4sXbt2qV9+/Y9t3rq1aunDz74QN27d1eJEiW0detWDRgw4KmWVbVqVV2/ft18f3eC4OBgXb9+3SJ4f/bZZypVqpRq1qypKlWqyMfHRw0aNDBP9/Dw0KZNm/TWW28pf/78+uyzzzRu3DjVrl1bkvTuu++qQIECKlOmjLJmzaqwsDC5urpq06ZN8vf3V6NGjVSoUCF16NBBt2/floeHx1NtEwAAAACkBSYjqedoAXYQHR0tT09Pbdy4UZUrV7Z3OQAAAADwSAn5JSoq6oknG1+qM94AAAAAADxvBG8AAAAAAGyI4A0AAAAAgA0RvAEAAAAAsCGCNwAAAAAANkTwBgAAAADAhgjeAAAAAADYEMEbAAAAAAAbIngDAAAAAGBDBG+8cHx8fOxdAgAAAACkGII3XjgEbwAAAABpCcEbAAAAAAAbIngDAAAAAGBDBG8AAAAAAGyI4A0AAAAAgA0RvAEAAAAAsCGCNwAAAAAANkTwBgAAAADAhgjeAAAAAADYEMEbAAAAAAAbIngDAAAAAGBDBG8AAAAAAGyI4A0AAAAAgA0RvAEAAAAAsKH09i4ASGAYhiQpOjrazpUAAAAAwOMl5JaEHPM4BG+8MK5cuSJJ8vPzs3MlAAAAAJA8169fl6en52P7ELzxwsicObMk6cyZM0/8wcWLKzo6Wn5+fjp79qw8PDzsXQ6eEvsxbWA/pg3sx7SB/Zg2sB/ThpTaj4Zh6Pr168qZM+cT+xK88cJIl+7+kAOenp58kaUBHh4e7Mc0gP2YNrAf0wb2Y9rAfkwb2I9pQ0rsx+SeMGRwNQAAAAAAbIjgDQAAAACADRG88cJwcnLSoEGD5OTkZO9S8AzYj2kD+zFtYD+mDezHtIH9mDawH9MGe+xHk5Gcsc8BAAAAAMBT4Yw3AAAAAAA2RPAGAAAAAMCGCN4AAAAAANgQwRsvjKlTpyogIEDOzs569dVXtWPHDnuXhMfYtGmT6tatq5w5c8pkMmnp0qUW0w3D0MCBA5UjRw65uLioRo0aioyMtE+xSNKIESNUtmxZZcyYUdmyZVODBg105MgRiz63b99Wt27dlCVLFrm7u6tx48b6999/7VQxkjJt2jQVK1bM/CzS8uXLa/ny5ebp7MPUaeTIkTKZTOrVq5e5jX354hs8eLBMJpPFq2DBgubp7MPU49y5c3rnnXeUJUsWubi4KCgoSLt27TJP5/ecF19AQECi49FkMqlbt26Snv/xSPDGC2HhwoX68MMPNWjQIP31118qXry4atasqYsXL9q7NDzCjRs3VLx4cU2dOjXJ6aNHj9akSZM0ffp0bd++XW5ubqpZs6Zu3779nCvFo2zcuFHdunXTn3/+qdWrV+vu3bt68803dePGDXOfDz74QL/++qt+/PFHbdy4Uf/8848aNWpkx6rxsFdeeUUjR47U7t27tWvXLlWrVk3169fXwYMHJbEPU6OdO3fq66+/VrFixSza2ZepQ5EiRXT+/Hnza8uWLeZp7MPU4b///lPFihWVIUMGLV++XIcOHdK4ceOUKVMmcx9+z3nx7dy50+JYXL16tSSpadOmkuxwPBrAC6BcuXJGt27dzO/j4uKMnDlzGiNGjLBjVUguScaSJUvM7+Pj4w0fHx9jzJgx5rZr164ZTk5Oxg8//GCHCpEcFy9eNCQZGzduNAzj/j7LkCGD8eOPP5r7REREGJKMbdu22atMJEOmTJmMWbNmsQ9ToevXrxv58uUzVq9ebQQHBxs9e/Y0DIPjMbUYNGiQUbx48SSnsQ9Tj48//th4/fXXHzmd33NSp549exqBgYFGfHy8XY5HznjD7u7cuaPdu3erRo0a5rZ06dKpRo0a2rZtmx0rw9M6efKkLly4YLFPPT099eqrr7JPX2BRUVGSpMyZM0uSdu/erbt371rsx4IFC8rf35/9+IKKi4vTggULdOPGDZUvX559mAp169ZNderUsdhnEsdjahIZGamcOXMqT548atWqlc6cOSOJfZia/PLLLypTpoyaNm2qbNmyqWTJkpo5c6Z5Or/npD537tzRvHnz1L59e5lMJrscjwRv2N3ly5cVFxen7NmzW7Rnz55dFy5csFNVeBYJ+419mnrEx8erV69eqlixoooWLSrp/n50dHSUl5eXRV/244tn//79cnd3l5OTk7p06aIlS5aocOHC7MNUZsGCBfrrr780YsSIRNPYl6nDq6++qtDQUK1YsULTpk3TyZMnValSJV2/fp19mIqcOHFC06ZNU758+bRy5Uq999576tGjh+bMmSOJ33NSo6VLl+ratWsKCQmRZJ/v1PQ2WSoAIFXp1q2bDhw4YHEvIlKPAgUKKDw8XFFRUVq8eLHatm2rjRs32rssWOHs2bPq2bOnVq9eLWdnZ3uXg6dUu3Zt87+LFSumV199Vbly5dKiRYvk4uJix8pgjfj4eJUpU0bDhw+XJJUsWVIHDhzQ9OnT1bZtWztXh6fxzTffqHbt2sqZM6fdauCMN+zO29tbDg4OiUYR/Pfff+Xj42OnqvAsEvYb+zR16N69u3777TetX79er7zyirndx8dHd+7c0bVr1yz6sx9fPI6OjsqbN69Kly6tESNGqHjx4po4cSL7MBXZvXu3Ll68qFKlSil9+vRKnz69Nm7cqEmTJil9+vTKnj07+zIV8vLyUv78+XXs2DGOx1QkR44cKly4sEVboUKFzLcN8HtO6nL69GmtWbNGHTt2NLfZ43gkeMPuHB0dVbp0aa1du9bcFh8fr7Vr16p8+fJ2rAxPK3fu3PLx8bHYp9HR0dq+fTv79AViGIa6d++uJUuWaN26dcqdO7fF9NKlSytDhgwW+/HIkSM6c+YM+/EFFx8fr9jYWPZhKlK9enXt379f4eHh5leZMmXUqlUr87/Zl6lPTEyMjh8/rhw5cnA8piIVK1ZM9HjNo0ePKleuXJL4PSe1mT17trJly6Y6deqY2+xyPNpkyDbASgsWLDCcnJyM0NBQ49ChQ0anTp0MLy8v48KFC/YuDY9w/fp1Y8+ePcaePXsMScb48eONPXv2GKdPnzYMwzBGjhxpeHl5GcuWLTP27dtn1K9f38idO7dx69YtO1eOBO+9957h6elpbNiwwTh//rz5dfPmTXOfLl26GP7+/sa6deuMXbt2GeXLlzfKly9vx6rxsH79+hkbN240Tp48aezbt8/o16+fYTKZjFWrVhmGwT5MzR4c1dww2JepQe/evY0NGzYYJ0+eNMLCwowaNWoY3t7exsWLFw3DYB+mFjt27DDSp09vDBs2zIiMjDTmz59vuLq6GvPmzTP34fec1CEuLs7w9/c3Pv7440TTnvfxSPDGC2Py5MmGv7+/4ejoaJQrV874888/7V0SHmP9+vWGpESvtm3bGoZx/1EbAwYMMLJnz244OTkZ1atXN44cOWLfomEhqf0nyZg9e7a5z61bt4yuXbsamTJlMlxdXY2GDRsa58+ft1/RSKR9+/ZGrly5DEdHRyNr1qxG9erVzaHbMNiHqdnDwZt9+eJr3ry5kSNHDsPR0dHw9fU1mjdvbhw7dsw8nX2Yevz6669G0aJFDScnJ6NgwYLGjBkzLKbze07qsHLlSkNSkvvmeR+PJsMwDNucSwcAAAAAANzjDQAAAACADRG8AQAAAACwIYI3AAAAAAA2RPAGAAAAAMCGCN4AAAAAANgQwRsAAAAAABsieAMAAAAAYEMEbwAAAAAAbIjgDQAAUr3Bgwcre/bsMplMWrp0qb3LSXNat26t4cOHP9MyWrRooXHjxqVQRQCQuhC8AQCwgZCQEJlM/9fevQdFVb9/AH8vl11WQAXkmlyMBbyBimIhFnIRsGIQg0ZlFPNCOCJFEZY6CJaVl6ZGGlG7oJYKqWCOqHFRFNFgQS4iCERcNFfJ8dbCxm2f3x+O5+cGiH2V/Or3ec3wx/l8zvmc5/Oc/YNnzzmfFUEkEkEsFkMmk2Ht2rXo6up60qH162krXqurq5GYmIht27ZBoVBgxowZPfZpbGwUrodIJIKxsTE8PT2Rn5//BCJ+upSXl+PIkSOIjo4W2jZt2gQzMzOYmZn1KKYLCwsxceLEHp/11atXY926dbh9+/a/EjdjjP034cKbMcYYGyABAQFQKBSoq6vDe++9h4SEBGzcuPE/Gqu7uxtqtfoxR/hsqK+vBwAEBQXBwsICEomkz31zcnKgUChw6tQpWFlZ4bXXXsO1a9f+rVCfSklJSQgNDYWBgQEAoKKiAvHx8UhNTcXevXuxevVqnD9/HgDQ1dWFyMhIbN26FTo6OhrjjB07Fvb29vjhhx/+9TkwxtiTxoU3Y4wxNkAkEgksLCxga2uLpUuXwtfXF4cOHQIAtLe3IzY2Fs899xz09fXxwgsvIC8vTzh2x44dGDp0KA4dOoTRo0dDIpGgubkZ7e3tWLFiBaytrSGRSCCTyfDtt98Kx1VWVmLGjBkwMDCAubk55s2bh+vXrwv906ZNQ3R0NOLi4mBsbAwLCwskJCQI/XZ2dgCA4OBgiEQiYbu+vh5BQUEwNzeHgYEB3NzckJOTozFfhUKBV199FVKpFCNGjMCePXtgZ2eHL7/8Utjn1q1bWLx4MUxNTTF48GB4e3ujvLz8gXk8f/48vL29IZVKYWJigoiICCiVSgB3HzEPDAwEAGhpaUEkEj1wLBMTE1hYWGDs2LFYuXIl7ty5g8LCwofO3/79++Hs7CzE4uvri9bWVgB3n3KYOXMmEhMThflFRkaio6NDOL69vR3R0dEwMzODnp4epk6dCrlcLvTn5eVBJBIhNzcXkyZNwqBBgzBlyhTU1NQI+5SXl8PLywuGhoYYPHgwJk6ciOLiYqH/9OnTeOmllyCVSmFtbY3o6GghRgDYsmULHBwcoKenB3Nzc4SEhPSZr+7ubuzfv1/IMQBcvHgRLi4u8Pb2ho+PD1xcXHDx4kUAwMaNG/Hyyy/Dzc2t1/ECAwORmpra9wVijLFnFBfejDHG2L9EKpUKRVhUVBTOnj2L1NRUVFRUIDQ0FAEBAairqxP2b2trw/r16/HNN9/gwoULMDMzw/z587F3715s3rwZ1dXV2LZtm3An8tatW/D29saECRNQXFyMY8eO4dq1a3jjjTc04ti5cyf09fVRWFiIDRs2YO3atcjOzgYAoQhMSUmBQqEQtpVKJV555RXk5uaitLQUAQEBCAwMRHNzszDu/PnzceXKFeTl5eHAgQPYvn07WlpaNM4dGhqKlpYWHD16FCUlJXB1dYWPjw9u3LjRa85aW1vh7+8PIyMjyOVy7Nu3Dzk5OYiKigIAxMbGIiUlBcDdwl+hUDzUtVCpVNi1axcAQCwWP1T+FAoF5syZg4ULF6K6uhp5eXmYNWsWiEgYNzc3V+jbu3cv0tPTkZiYKPTHxcXhwIED2LlzJ86dOweZTAZ/f/8e81+1ahU+//xzFBcXQ0dHBwsXLhT6wsLCMHz4cMjlcpSUlOCDDz6Arq4ugLtfkAQEBOD1119HRUUF0tLScPr0aSFfxcXFiI6Oxtq1a1FTU4Njx47h5Zdf7jNPFRUVuH37NiZNmiS0OTs7o7a2Fs3NzWhqakJtbS3Gjh2L+vp6pKSk4OOPP+5zvMmTJ6OoqAjt7e0PvkCMMfasIcYYY4w9duHh4RQUFERERGq1mrKzs0kikVBsbCw1NTWRtrY2/f777xrH+Pj40IcffkhERCkpKQSAysrKhP6amhoCQNnZ2b2e86OPPiI/Pz+NtkuXLhEAqqmpISIiT09Pmjp1qsY+bm5utGLFCmEbAGVkZPQ7xzFjxlBSUhIREVVXVxMAksvlQn9dXR0BoC+++IKIiPLz82nw4MH0119/aYxjb29P27Zt6/Uc27dvJyMjI1IqlUJbZmYmaWlp0dWrV4mIKCMjg/r7l6ahoYEAkFQqJX19fRKJRASAJk6cSB0dHUTUf/5KSkoIADU2NvZ6jvDwcDI2NqbW1lahLTk5mQwMDKi7u5uUSiXp6urS7t27hf6Ojg6ysrKiDRs2EBHRiRMnCADl5ORozBcAqVQqIiIyNDSkHTt29BrDokWLKCIiQqMtPz+ftLS0SKVS0YEDB2jw4MF0586dB+brnoyMDNLW1ia1Wq3RnpycTI6OjuTo6EjJyclEdPfzm5GRQfv27aMxY8bQ+PHj6eTJkxrHlZeXPzCHjDH2rNLprRhnjDHG2KM7fPgwDAwM0NnZCbVajblz5yIhIQF5eXno7u6Go6Ojxv7t7e0wMTERtsViMVxcXITtsrIyaGtrw9PTs9fzlZeX48SJE8Id8PvV19cL57t/TACwtLTscWf675RKJRISEpCZmQmFQoGuri6oVCrhjndNTQ10dHTg6uoqHCOTyWBkZKQRn1Kp1JgjcPfu8733tP+uuroa48aNg76+vtDm4eEBtVqNmpoamJubPzDuv0tLS8PIkSNRWVmJuLg47NixQ7hb3F/+/Pz84OPjA2dnZ/j7+8PPzw8hISEacxw3bhwGDRokbLu7u0OpVOLSpUu4ffs2Ojs74eHhIfTr6upi8uTJqK6u1jjf/dfI0tISANDS0gIbGxu8++67WLx4Mb7//nv4+voiNDQU9vb2whwqKiqwe/du4XgiglqtRkNDA6ZPnw5bW1s8//zzCAgIQEBAAIKDgzVivp9KpYJEIunxCH9kZCQiIyOF7Z07d8LQ0BDu7u5wcnKCXC7H5cuXMXv2bDQ0NAjv3UulUgB3n+ZgjLH/JVx4M8YYYwPEy8sLycnJEIvFsLKyEhabUiqV0NbWRklJCbS1tTWOub/ok0qlGgXPvaKlL0qlEoGBgVi/fn2PvnvFGwCh0LxHJBL1u3BbbGwssrOzsWnTJshkMkilUoSEhGi8v9wfpVIJS0tLjXfZ7xk6dOhDj/MorK2t4eDgAAcHB3R1dSE4OBiVlZWQSCT95k9bWxvZ2dk4c+YMsrKykJSUhFWrVqGwsBAjRox4rHHef43ufQbuXaOEhATMnTsXmZmZOHr0KNasWYPU1FQEBwdDqVTirbfe0liB/B4bGxuIxWKcO3cOeXl5yMrKQnx8PBISEiCXy3u9BsOGDUNbWxs6OjqER/L/7vr160hMTMSpU6dQWFgIR0dHIcednZ2ora2Fs7MzAAiP1Juamj5Sfhhj7GnD73gzxhhjA0RfXx8ymQw2NjYaKzxPmDAB3d3daGlpgUwm0/izsLDoczxnZ2eo1WqcPHmy135XV1dcuHABdnZ2Pca9/45xf3R1ddHd3a3RVlBQgAULFiA4OBjOzs6wsLBAY2Oj0O/k5ISuri6UlpYKbb/++itu3rypEd/Vq1eho6PTI75hw4b1GsuoUaNQXl6usThYQUEBtLS04OTk9NBz6k1ISAh0dHSwZcsWIb7+8icSieDh4YHExESUlpZCLBYjIyNDGLO8vBwqlUrY/uWXX2BgYABra2vY29tDLBajoKBA6O/s7IRcLsfo0aP/UeyOjo6IiYlBVlYWZs2aJbzn7urqiqqqqh7xy2QyoXDW0dGBr68vNmzYgIqKCjQ2NuL48eO9nmf8+PEAgKqqqj5jiYmJQUxMDIYPH47u7m50dnYKfV1dXRqfpcrKSgwfPrzP680YY88qLrwZY4yxf5mjoyPCwsIwf/58pKeno6GhAUVFRfj000+RmZnZ53F2dnYIDw/HwoULcfDgQTQ0NCAvLw8//vgjAGDZsmW4ceMG5syZA7lcjvr6evz888948803exTSD2JnZ4fc3FxcvXpVKJwdHByQnp6OsrIylJeXY+7cuRp3yUeOHAlfX19ERESgqKgIpaWliIiI0Lhr7+vrC3d3d8ycORNZWVlobGzEmTNnsGrVKo1Vue8XFhYGPT09hIeHo7KyEidOnMDy5csxb968f/yY+d+JRCJER0fjs88+Q1tbW7/5KywsxCeffILi4mI0NzcjPT0df/zxB0aNGiWM2dHRgUWLFqGqqgpHjhzBmjVrEBUVBS0tLejr62Pp0qV4//33cezYMVRVVWHJkiVoa2vDokWLHipmlUqFqKgo5OXloampCQUFBZDL5UIMK1aswJkzZxAVFYWysjLU1dXhp59+EhZXO3z4MDZv3oyysjI0NTVh165dUKvVfX6JYWpqCldXV5w+fbrX/uzsbNTW1mLZsmUAADc3N1y8eBFHjx7F9u3boa2trTF2fn4+/Pz8HmqujDH2THnSL5kzxhhjz6L7F1frTUdHB8XHx5OdnR3p6uqSpaUlBQcHU0VFBRHdXVxtyJAhPY5TqVQUExNDlpaWJBaLSSaT0XfffSf019bWUnBwMA0dOpSkUimNHDmS3nnnHWFxLE9PT3r77bc1xgwKCqLw8HBh+9ChQySTyUhHR4dsbW2J6O7iZF5eXiSVSsna2pq++uqrHmNduXKFZsyYQRKJhGxtbWnPnj1kZmZGW7duFfa5c+cOLV++nKysrEhXV5esra0pLCyMmpub+8xVRUUFeXl5kZ6eHhkbG9OSJUvozz//FPr/yeJqpaWlGu2tra1kZGRE69ev7zd/VVVV5O/vT6ampiSRSMjR0VFYXI7o/695fHw8mZiYkIGBAS1ZskRjMTmVSkXLly+nYcOGkUQiIQ8PDyoqKhL67y2udvPmTaGttLSUAFBDQwO1t7fT7NmzydramsRiMVlZWVFUVJSw8BoRUVFREU2fPp0MDAxIX1+fXFxcaN26dUR0d6E1T09PMjIyIqlUSi4uLpSWlvbA3G3ZsoVefPHFHu1tbW3k6OjYI6dff/01mZubk42NDR0+fFhj7kOGDKGzZ88+8HyMMfYsEhHd9xsYjDHGGGOPyeXLl2FtbY2cnBz4+Pg86XAG3IIFC3Dr1i0cPHjwSYfyWKlUKjg5OSEtLQ3u7u7/8TjJycnIyMhAVlbWY4yOMcaeDry4GmOMMcYei+PHj0OpVMLZ2RkKhQJxcXGws7N74O9Es/9+UqkUu3btwvXr1x9pHF1dXSQlJT2mqBhj7OnChTdjjDHGHovOzk6sXLkSv/32GwwNDTFlyhTs3r27xyrq7Okzbdq0Rx5j8eLFjx4IY4w9pfhRc8YYY4wxxhhjbADxquaMMcYYY4wxxtgA4sKbMcYYY4wxxhgbQFx4M8YYY4wxxhhjA4gLb8YYY4wxxhhjbABx4c0YY4wxxhhjjA0gLrwZY4wxxhhjjLEBxIU3Y4wxxhhjjDE2gLjwZowxxhhjjDHGBhAX3owxxhhjjDHG2AD6P8dThZHty/qjAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Extract the sum of responses for each column\n",
        "responses = df_gen_survey[['cooking_energy_source_grid_electricity',\n",
        "                           'cooking_energy_source_charcoal',\n",
        "                           'cooking_energy_source_energy_briquettes',\n",
        "                           'cooking_energy_source_gas',\n",
        "                           'cooking_energy_source_firewood',\n",
        "                           'cooking_energy_source_animal_waste',\n",
        "                           'cooking_energy_source_None',\n",
        "                           'cooking_energy_source_other']].sum()\n",
        "\n",
        "# Convert to percentages\n",
        "responses_percentage = (responses / responses.sum()) * 100\n",
        "\n",
        "# Rename the index for cleaner labels\n",
        "responses_percentage.index = ['Grid Electricity', 'Charcoal', 'Energy Briquettes', 'Gas',\n",
        "                              'Firewood', 'Animal Waste', 'None', 'Other']\n",
        "\n",
        "# Create a horizontal bar plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "responses_percentage.sort_values().plot(kind='barh', color='skyblue', edgecolor='black')\n",
        "\n",
        "# Adding labels and title\n",
        "plt.xlabel('Percentage of Responses (%)')\n",
        "plt.ylabel('Cooking Energy Source')\n",
        "plt.title('Percentage of Responses for Different Cooking Energy Sources')\n",
        "\n",
        "# Display the plot\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8K3oo0e6Sa0F"
      },
      "outputs": [],
      "source": [
        "# Define the list of appliances\n",
        "appliance_list = [\n",
        "    'Sigiri (traditional, charcoal)', 'Sigiri (high-efficiency, charcoal)', 'Pressure cooker (charcoal)',\n",
        "    'Cooking coils', 'Hot plate', 'Percolator', 'Pressure cooker (electric)', 'Rice cooker', 'Stove (electric)',\n",
        "    'Microwave', 'Oven (electric)', 'Blender', 'Juicer', 'Deep fryer (electric)', 'Popcorn machine',\n",
        "    'Ice cream machine', 'Stones (firewood)', 'Sigiri (traditional, energy briquettes)',\n",
        "    'Sigiri (high-efficiency, energy briquettes)', 'Stove (kerosene)', 'Pressure cooker (gas)', 'Stove (gas)',\n",
        "    'Oven (gas)', 'Other'\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "js5yPymqon0B"
      },
      "source": [
        "## Overall appliance and fuel source use"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z4LG7JSKfK9F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1KQyk9Vk_mIA"
      },
      "outputs": [],
      "source": [
        "def create_pivot_table(df, groupby_columns, value_column):\n",
        "    \"\"\"\n",
        "    Generate pivot tables for counts and percentages.\n",
        "\n",
        "    Args:\n",
        "        df (pd.DataFrame): The DataFrame containing the data.\n",
        "        groupby_columns (list): List of columns to group by (e.g., ['Category', 'Subcategory']).\n",
        "        value_column (str): The column for which counts and percentages are calculated.\n",
        "\n",
        "    Returns:\n",
        "        tuple: A tuple containing:\n",
        "            - count_pivot (pd.DataFrame): Pivot table of counts.\n",
        "            - percentage_pivot (pd.DataFrame): Pivot table of percentages.\n",
        "    \"\"\"\n",
        "    # Step 1: Calculate counts for each group\n",
        "    grouped_counts = df.groupby(groupby_columns).size().reset_index(name='Count')\n",
        "    grouped_counts['Count'] = grouped_counts['Count'].astype(int)\n",
        "\n",
        "    # Step 2: Calculate total counts for each main group\n",
        "    total_counts = grouped_counts.groupby(groupby_columns[0])['Count'].sum().rename('Total_Count')\n",
        "\n",
        "    # Step 3: Merge total counts back to calculate percentages\n",
        "    grouped_counts = grouped_counts.merge(total_counts, on=groupby_columns[0])\n",
        "    grouped_counts['Percentage'] = ((grouped_counts['Count'] / grouped_counts['Total_Count']) * 100).round(2)\n",
        "\n",
        "    # Step 4: Create a pivot table for counts\n",
        "    count_pivot = grouped_counts.pivot_table(\n",
        "        index=groupby_columns[0],\n",
        "        columns=groupby_columns[1],\n",
        "        values='Count',\n",
        "        fill_value=0,\n",
        "        aggfunc=\"sum\"  # Replace np.sum with \"sum\"\n",
        "    )\n",
        "\n",
        "    # Step 5: Create a pivot table for percentages\n",
        "    percentage_pivot = grouped_counts.pivot_table(\n",
        "        index=groupby_columns[0],\n",
        "        columns=groupby_columns[1],\n",
        "        values='Percentage',\n",
        "        fill_value=0,\n",
        "        aggfunc=\"mean\"  # Replace np.mean with \"mean\"\n",
        "    )\n",
        "\n",
        "    return count_pivot, percentage_pivot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yzze2eGz_ziw"
      },
      "outputs": [],
      "source": [
        "# Helper function to create a custom legend\n",
        "class LegendTitle(object):\n",
        "    def __init__(self, text_props=None):\n",
        "        self.text_props = text_props or {}\n",
        "        super(LegendTitle, self).__init__()\n",
        "\n",
        "    def legend_artist(self, legend, orig_handle, fontsize, handlebox):\n",
        "        x0, y0 = handlebox.xdescent, handlebox.ydescent\n",
        "        title = mtext.Text(x0, y0, orig_handle, **self.text_props)\n",
        "        handlebox.add_artist(title)\n",
        "        return title"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sWmTyGRt9PoB"
      },
      "outputs": [],
      "source": [
        "# Generate pivot tables for meals without any printing or displaying\n",
        "\n",
        "df_breakfast = df_cooking_survey['breakfast_home_cooking_modal...']\n",
        "df_lunch = df_cooking_survey['lunch_home_cooking_modality_...']\n",
        "df_dinner = df_cooking_survey['dinner_home_cooking_modality...']\n",
        "df_snacks = df_cooking_survey['snacks_home_cooking_modality...']\n",
        "\n",
        "# Breakfast\n",
        "breakfast_data = df_breakfast[['breakfast_home_list', 'When you prepare ${breakfast_home_list} at home, how do you most often cook it?']]\n",
        "breakfast_count, breakfast_percentage = create_pivot_table(\n",
        "    breakfast_data,\n",
        "    ['breakfast_home_list', 'When you prepare ${breakfast_home_list} at home, how do you most often cook it?'],\n",
        "    'Count'\n",
        ")\n",
        "\n",
        "# Lunch\n",
        "lunch_data = df_lunch[['lunch_home_list', 'When you prepare ${lunch_home_list} at home, how do you most often cook it?']]\n",
        "lunch_count, lunch_percentage = create_pivot_table(\n",
        "    lunch_data,\n",
        "    ['lunch_home_list', 'When you prepare ${lunch_home_list} at home, how do you most often cook it?'],\n",
        "    'Count'\n",
        ")\n",
        "\n",
        "# Dinner\n",
        "dinner_data = df_dinner[['dinner_home_list', 'When you prepare ${dinner_home_list} at home, how do you most often cook it?']]\n",
        "dinner_count, dinner_percentage = create_pivot_table(\n",
        "    dinner_data,\n",
        "    ['dinner_home_list', 'When you prepare ${dinner_home_list} at home, how do you most often cook it?'],\n",
        "    'Count'\n",
        ")\n",
        "\n",
        "# Snacks\n",
        "snacks_data = df_snacks[['snacks_home_list', 'When you prepare ${snacks_home_list} at home, how do you most often cook it?']]\n",
        "snacks_count, snacks_percentage = create_pivot_table(\n",
        "    snacks_data,\n",
        "    ['snacks_home_list', 'When you prepare ${snacks_home_list} at home, how do you most often cook it?'],\n",
        "    'Count'\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LR9qkT_H22pQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "2d540733-e6c5-4df9-fbbe-9fe9ea6f2c7a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    lunch_home_list  \\\n",
              "0           Matooke   \n",
              "1             Posho   \n",
              "2             Other   \n",
              "3             Posho   \n",
              "4              Rice   \n",
              "..              ...   \n",
              "616         Matooke   \n",
              "617            Gnut   \n",
              "618         Chicken   \n",
              "619            Fish   \n",
              "620            Beef   \n",
              "\n",
              "    When you prepare ${lunch_home_list} at home, how do you most often cook it?  \n",
              "0                       Sigiri (traditional, charcoal)                           \n",
              "1                       Sigiri (traditional, charcoal)                           \n",
              "2                       Sigiri (traditional, charcoal)                           \n",
              "3                       Sigiri (traditional, charcoal)                           \n",
              "4                       Sigiri (traditional, charcoal)                           \n",
              "..                                                 ...                           \n",
              "616                                          Hot plate                           \n",
              "617                                          Hot plate                           \n",
              "618                                          Hot plate                           \n",
              "619                                          Hot plate                           \n",
              "620                                          Hot plate                           \n",
              "\n",
              "[621 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-27ad2b00-7162-4bb9-80b3-7ff56c1caa2a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lunch_home_list</th>\n",
              "      <th>When you prepare ${lunch_home_list} at home, how do you most often cook it?</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Matooke</td>\n",
              "      <td>Sigiri (traditional, charcoal)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Posho</td>\n",
              "      <td>Sigiri (traditional, charcoal)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Other</td>\n",
              "      <td>Sigiri (traditional, charcoal)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Posho</td>\n",
              "      <td>Sigiri (traditional, charcoal)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Rice</td>\n",
              "      <td>Sigiri (traditional, charcoal)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>616</th>\n",
              "      <td>Matooke</td>\n",
              "      <td>Hot plate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>617</th>\n",
              "      <td>Gnut</td>\n",
              "      <td>Hot plate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>618</th>\n",
              "      <td>Chicken</td>\n",
              "      <td>Hot plate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>619</th>\n",
              "      <td>Fish</td>\n",
              "      <td>Hot plate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>620</th>\n",
              "      <td>Beef</td>\n",
              "      <td>Hot plate</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>621 rows × 2 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-27ad2b00-7162-4bb9-80b3-7ff56c1caa2a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-27ad2b00-7162-4bb9-80b3-7ff56c1caa2a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-b734fb26-37eb-4155-be5a-8c9de3516379 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_3aac6adc-0d81-4f65-8527-c71d26bd23a6\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('lunch_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_3aac6adc-0d81-4f65-8527-c71d26bd23a6 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('lunch_data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "lunch_data",
              "summary": "{\n  \"name\": \"lunch_data\",\n  \"rows\": 621,\n  \"fields\": [\n    {\n      \"column\": \"lunch_home_list\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"Matooke\",\n          \"Posho\",\n          \"Beef\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"When you prepare ${lunch_home_list} at home, how do you most often cook it?\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"Stones (firewood)\",\n          \"Cooking coils\",\n          \"Sigiri (traditional, charcoal)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "display(lunch_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jpDAW5l0GWUT"
      },
      "outputs": [],
      "source": [
        "# Define column order and corresponding colors and fuel sources for appliances\n",
        "appliance_colors = {\n",
        "    'Hot plate': '#101915',\n",
        "    'Cooking coils': '#115534',\n",
        "    'Pressure cooker (electric)': '#60BE92',\n",
        "    'Percolator': '#46AF42',\n",
        "    'Rice cooker': '#32C52A',\n",
        "    'Stove (electric)': '#93D62D',\n",
        "    'Oven (electric)': '#B7E025',\n",
        "    'Deep fryer (electric)': '#D3F068',\n",
        "    'Pressure cooker (charcoal)': '#900202',\n",
        "    'Sigiri (traditional, charcoal)': '#d00000',\n",
        "    'Sigiri (high-efficiency, charcoal)': '#f8aab9',\n",
        "    'Stones (firewood)': '#984309',\n",
        "    'Oven (gas)': '#c16e08',\n",
        "    'Pressure cooker (gas)': '#eebb0a',\n",
        "    'Stove (gas)': '#ffe582',\n",
        "    'Sigiri (traditional, energy briquettes)': '#4a0376',\n",
        "    'Sigiri (high-efficiency, energy briquettes)': '#424bb2',\n",
        "    'Stove (kerosene)': '#82c1f7',\n",
        "    'Other': '#9370dc'\n",
        "}\n",
        "\n",
        "appliance_fuel_source = {\n",
        "    'Hot plate': 'Electricity',\n",
        "    'Cooking coils': 'Electricity',\n",
        "    'Pressure cooker (electric)': 'Electricity',\n",
        "    'Percolator': 'Electricity',\n",
        "    'Rice cooker': 'Electricity',\n",
        "    'Stove (electric)': 'Electricity',\n",
        "    'Oven (electric)': 'Electricity',\n",
        "    'Deep fryer (electric)': 'Electricity',\n",
        "    'Pressure cooker (charcoal)': 'Charcoal',\n",
        "    'Sigiri (traditional, charcoal)': 'Charcoal',\n",
        "    'Sigiri (high-efficiency, charcoal)': 'Charcoal',\n",
        "    'Stones (firewood)': 'Firewood',\n",
        "    'Oven (gas)': 'Gas',\n",
        "    'Pressure cooker (gas)': 'Gas',\n",
        "    'Stove (gas)': 'Gas',\n",
        "    'Sigiri (traditional, energy briquettes)': 'Other',\n",
        "    'Sigiri (high-efficiency, energy briquettes)': 'Other',\n",
        "    'Stove (kerosene)': 'Other',\n",
        "    'Other': 'Other'\n",
        "}\n",
        "\n",
        "appliance_fuel_source_labels = {\n",
        "    'Hot plate': 'Hot plate',\n",
        "    'Cooking coils': 'Cooking coils',\n",
        "    'Pressure cooker (electric)': 'Pressure cooker',\n",
        "    'Percolator': 'Electric kettle',\n",
        "    'Rice cooker': 'Rice cooker',\n",
        "    'Stove (electric)': 'Stove',\n",
        "    'Oven (electric)': 'Oven',\n",
        "    'Deep fryer (electric)': 'Deep fryer',\n",
        "    'Pressure cooker (charcoal)': 'Pressure cooker',\n",
        "    'Sigiri (traditional, charcoal)': 'Sigiri (traditional)',\n",
        "    'Sigiri (high-efficiency, charcoal)': 'Sigiri (high-efficiency)',\n",
        "    'Stones (firewood)': 'Stones',\n",
        "    'Oven (gas)': 'Oven',\n",
        "    'Pressure cooker (gas)': 'Pressure cooker',\n",
        "    'Stove (gas)': 'Stove',\n",
        "    'Sigiri (traditional, energy briquettes)': 'Sigiri (traditional, energy briquettes)',\n",
        "    'Sigiri (high-efficiency, energy briquettes)': 'Sigiri (high-efficiency, energy briquettes)',\n",
        "    'Stove (kerosene)': 'Stove (kerosene)',\n",
        "    'Other': 'Other'\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AwywRXtdQ2KK"
      },
      "source": [
        "## Appliance use by food and meal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ic3UWfhY-F2z"
      },
      "outputs": [],
      "source": [
        "def process_and_visualize_meals(\n",
        "    breakfast_count, breakfast_percentage,\n",
        "    lunch_count, lunch_percentage,\n",
        "    dinner_count, dinner_percentage,\n",
        "    snacks_count, snacks_percentage,\n",
        "    row_heights=[1, 4, 4, 3],  # Specify height for each row\n",
        "    label_x=0.01,  # X position for meal labels\n",
        "    label_y_offsets=[.993, 0.888, 0.583, 0.273]  # Y position offsets for meal labels\n",
        "):\n",
        "    # Group appliances by fuel source for the legend\n",
        "    grouped_labels = {}\n",
        "    for appliance, fuel in appliance_fuel_source.items():\n",
        "        if fuel not in grouped_labels:\n",
        "            grouped_labels[fuel] = []\n",
        "        grouped_labels[fuel].append(appliance)\n",
        "\n",
        "    # Reorder columns to match legend order, ensuring only valid columns are included\n",
        "    def reorder_columns(data):\n",
        "        valid_appliances = [appliance for appliance in ordered_appliances if appliance in data.columns]\n",
        "        return data[valid_appliances]\n",
        "\n",
        "    # Use the display labels for legend\n",
        "    ordered_appliances = [appliance for group in grouped_labels.values() for appliance in group]\n",
        "\n",
        "    breakfast_count = reorder_columns(breakfast_count)\n",
        "    lunch_count = reorder_columns(lunch_count)\n",
        "    dinner_count = reorder_columns(dinner_count)\n",
        "    snacks_count = reorder_columns(snacks_count)\n",
        "\n",
        "    # Define colors for the appliances in the plots\n",
        "    color_list_breakfast = [appliance_colors[appliance] for appliance in breakfast_count.columns]\n",
        "    color_list_lunch = [appliance_colors[appliance] for appliance in lunch_count.columns]\n",
        "    color_list_dinner = [appliance_colors[appliance] for appliance in dinner_count.columns]\n",
        "    color_list_snacks = [appliance_colors[appliance] for appliance in snacks_count.columns]\n",
        "\n",
        "    # Rename index\n",
        "    for count_df in [breakfast_count, lunch_count, dinner_count, snacks_count]:\n",
        "        count_df.rename(index={'Boiled water (not for porridge, tea, or coffee)': 'Boiled water'}, inplace=True)\n",
        "\n",
        "    # Sort the bars in ascending order by row-wise sum\n",
        "    breakfast_count = breakfast_count.loc[breakfast_count.sum(axis=1).sort_values(ascending=True).index]\n",
        "    lunch_count = lunch_count.loc[lunch_count.sum(axis=1).sort_values(ascending=True).index]\n",
        "    dinner_count = dinner_count.loc[dinner_count.sum(axis=1).sort_values(ascending=True).index]\n",
        "    snacks_count = snacks_count.loc[snacks_count.sum(axis=1).sort_values(ascending=True).index]\n",
        "\n",
        "    # Calculate appliance percentages\n",
        "    breakfast_percentages = breakfast_count.div(breakfast_count.sum(axis=1), axis=0) * 100\n",
        "    lunch_percentages = lunch_count.div(lunch_count.sum(axis=1), axis=0) * 100\n",
        "    dinner_percentages = dinner_count.div(dinner_count.sum(axis=1), axis=0) * 100\n",
        "    snacks_percentages = snacks_count.div(snacks_count.sum(axis=1), axis=0) * 100\n",
        "\n",
        "    # Adjust figure height based on row heights\n",
        "    total_height = sum(row_heights)\n",
        "    fig = plt.figure(figsize=(12, total_height))  # Adjusted total figure height\n",
        "    fig.patch.set_facecolor('white')\n",
        "\n",
        "    # Define GridSpec with custom row heights\n",
        "    gs = GridSpec(4, 2, height_ratios=row_heights, width_ratios=[1, 1])\n",
        "\n",
        "    # Meal labels\n",
        "    meal_labels = ['Breakfast', 'Lunch', 'Dinner', 'Snacks']\n",
        "    meal_counts = [breakfast_count, lunch_count, dinner_count, snacks_count]\n",
        "    meal_percentages = [breakfast_percentages, lunch_percentages, dinner_percentages, snacks_percentages]\n",
        "\n",
        "    percentage_formatter = FuncFormatter(lambda x, _: f\"{int(x)}%\")\n",
        "\n",
        "    for i, (label, count_df, percentage_df) in enumerate(zip(meal_labels, meal_counts, meal_percentages)):\n",
        "        # Add meal label with custom placement\n",
        "        fig.text(label_x, label_y_offsets[i], label, fontsize=14, fontweight='bold', ha='left', va='center')\n",
        "\n",
        "        color_list = (\n",
        "            color_list_breakfast if label == 'Breakfast' else\n",
        "            color_list_lunch if label == 'Lunch' else\n",
        "            color_list_dinner\n",
        "        )\n",
        "\n",
        "        # Overall percentage plot\n",
        "        ax1 = fig.add_subplot(gs[i, 0])\n",
        "        count_df.plot(kind='barh', stacked=True, ax=ax1, color=color_list, legend=False, zorder=3, width=0.6)\n",
        "        ax1.set_xlabel(None if i < len(row_heights) - 1 else 'Probability of food preparation', fontsize=12)\n",
        "        ax1.set_ylabel(None)\n",
        "        ax1.set_xlim(0, 100)\n",
        "        ax1.grid(axis='y', linestyle='-', linewidth=0.5, color='black', zorder=0)\n",
        "        ax1.tick_params(axis='x', labelsize=10)\n",
        "        ax1.xaxis.set_major_formatter(percentage_formatter)\n",
        "        ax1.tick_params(axis='y', labelsize=10)\n",
        "\n",
        "        # Appliance percentages plot\n",
        "        ax2 = fig.add_subplot(gs[i, 1])\n",
        "        percentage_df.plot(kind='barh', stacked=True, ax=ax2, color=color_list, legend=False, zorder=3, width=0.6)\n",
        "        ax2.set_xlabel(None if i < len(row_heights) - 1 else 'Cooking events by appliance', fontsize=12)\n",
        "        ax2.set_ylabel(None)\n",
        "        ax2.set_xlim(0, 100)\n",
        "        ax2.grid(axis='y', linestyle='-', linewidth=0.5, color='black', zorder=0)\n",
        "        ax2.set_yticklabels([])\n",
        "        ax2.tick_params(axis='x', labelsize=10)\n",
        "        ax2.xaxis.set_major_formatter(percentage_formatter)\n",
        "\n",
        "    # Add a single legend to the far right grouped by fuel source\n",
        "    handles = []\n",
        "    labels = []\n",
        "\n",
        "    # Adjust layout and show\n",
        "    plt.tight_layout(rect=[0, 0, 1, 1])\n",
        "\n",
        "    fig.savefig(fig_path + \"Food-Meal by Appliance.png\", dpi=500)\n",
        "\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xWd9YH0g_7_1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4f2efc4b-ac70-4edc-f99a-b3bfae5f2af6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 8 Axes>"
            ],
            "image/png": 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OHTVlyhSVLVtWkuTl5aUFCxZc8zG/iRMn6vHHH1dUVJS9rVatWpKklJQUxcbGKjExUY0bN5Z0uehWvnx5LV++XD169NBdd90lSSpdurQCAgIkKde2yZMnKyIiwr6IepUqVTRr1iw1a9ZMc+fO1bFjxxQdHa1Dhw7ZHxEcNWqUVq5cqejoaE2aNClH7JMnT3aIW7r8jTwAANwOOnXqZHYIN+16d4QXFeQSAAAgu9u2SHXu3Dnt27dPzz77rPr27Wtvz8jIkI+Pj317yZIlmjVrlvbt26fU1FRlZGTI29v7quMmJSWpVq1a9gKVJDVp0kRZWVlKTk62F6nCwsKuuw7V9u3bHWLLPo+Li4saNmxob/Pz81O1atWUlJR07ZPPZseOHdq5c6cWLlxobzMMQ1lZWTpw4ID279+vzMxMVa1a1eG4tLQ0+fn55TrmmDFjNGLECPu2zWZTWIO6NxQXAABFVWxsrNkh3DSbzeaQ6xRV5BIAACC727ZIdWWNqPnz5zsUeiTJ2dlZkrRhwwZFREQoKipK7dq1k4+PjxYvXqzp06ff8vz/LGJdjYeHxy3Pkxepqal6/vnnNXTo0Bz7KlSooJ07d8rZ2Vlbt261vzdXXG3heavVKqvVWiDxAgCA2x+5BAAAyO62LVKVLVtWQUFB2r9/vyIiInLts379egUHB+uVV16xt/3222/XHDc0NFQxMTE6d+6cvRCVmJgoJyenay6QnpuaNWsqPj5eTz/9dK7zZGRkaOPGjfbH/U6ePKnk5GTde++9NzRP3bp1tXv3boWEhOS6v06dOsrMzNSxY8fUtGnTGxobAAAAAAAgP9zWC6dHRUVp8uTJmjVrln799Vft2rVL0dHRmjFjhqTLazMdOnRIixcv1r59+zRr1iwtW7bsmmNGRETI3d1dffr00c8//6y1a9dqyJAheuqpp+yP+uXV+PHj9fHHH2v8+PFKSkrSrl27NGXKFHtsnTt3Vt++fbVu3Trt2LFDTz75pMqVK6fOnTvf0Dwvv/yy1q9fr8GDB2v79u1KSUnRF198YV84vWrVqoqIiFDv3r21dOlSHThwQJs2bdLkyZP11Vdf3dBcAAAAAAAAN+O2LlI999xzWrBggaKjoxUWFqZmzZopJiZGlSpVknR5UdQXXnhBgwcPVu3atbV+/XqNHTv2mmN6enpq1apVOnXqlO6//349+uijatWqlWbPnn3D8TVv3lyffvqpYmNjVbt2bbVs2VKbNm2y74+Ojla9evXUoUMHNWrUSIZhaMWKFXJ1db2heWrWrKnvvvtOv/76q5o2bao6depo3Lhx9kXSr8zVu3dvjRw5UtWqVVOXLl20efNmVahQ4YbPCwAAAAAA4EZZDMMwzA4Cxd+VRVrPnDlzzYXnAQBAwSqun8nFNW4AAG43Zn4m39Z3UgEAAAAAAKB4oEgFAAAAAAAA01GkAgAAAAAAgOkoUgEAAAAAAMB0FKkAAAAAAABgOopUAAAAAAAAMB1FKgAAAAAAAJiOIhUAAAAAAABM52J2AMi7gwcPqlKlStq2bZtq166da5+EhAS1aNFCp0+flq+vb6HG5xvor+DqIYU6Z36zPBh8U8d1HtD7puf888zy6/bpWO14nsZqeu+um44D11b9k1Vmh1DknS/VNUdbqdbnTIgEQHF1O+QSxcHN5js341ZypBuVl5wqL/Kad10PeRnMRv6Kq8ktby8quJMqH0RGRspischiscjNzU0hISF67bXXlJGRka/zlC9fXkeOHFGNGjXydVwAAAAAAACzcSdVPgkPD1d0dLTS0tK0YsUKDRo0SK6urhozZswNjZOZmSmLxSInJ8f64aVLl+Tm5qaAgID8DBsAAAAAAKBI4E6qfGK1WhUQEKDg4GANGDBArVu3VmxsrE6fPq3evXurVKlS8vT0VPv27ZWSkmI/LiYmRr6+voqNjdW9994rq9WqQ4cOqWLFinr99dfVu3dveXt7q1+/fjp48KAsFou2b99uP37FihWqWrWqPDw81KJFCx08eDBHbPPnz1f58uXl6emprl27asaMGTkeBfziiy9Ut25dubu7q3LlyoqKisr3O8EAAAAAAACuhiJVAfHw8NClS5cUGRmpLVu2KDY2Vhs2bJBhGHr44YeVnp5u73v+/HlNmTJFCxYs0C+//KK77rpLkjRt2jTVqlVL27Zt09ixY3PMcfjwYXXr1k0dO3bU9u3b9dxzz2n06NEOfRITE9W/f38NGzZM27dvV5s2bTRx4kSHPj/88IN69+6tYcOGaffu3Zo3b55iYmJy9AMAAAAAACgoPO6XzwzDUHx8vFatWqX27dtr+fLlSkxMVOPGjSVJCxcuVPny5bV8+XL16NFDkpSenq45c+aoVq1aDmO1bNlSI0eOtG9nv0tq7ty5uueeezR9+nRJUrVq1bRr1y5NmTLF3uftt99W+/btNWrUKElS1apVtX79esXFxdn7REVFafTo0erTp48kqXLlynr99df10ksvafz48bmeZ1pamtLS0uzbNpvtht4nAABwZyOXAAAA2VGkyidxcXEqUaKE0tPTlZWVpSeeeELdunVTXFycGjZsaO/n5+enatWqKSkpyd7m5uammjVr5hizfv3615wzKSnJYWxJatSokcN2cnKyunZ1XLm/QYMGDkWqHTt2KDEx0eHOqczMTF28eFHnz5+Xp6dnjrknT56sqKgohzbfQP9rxgvgztOpUyezQwDuOP+8W7soI5cAAADZUaTKJy1atNDcuXPl5uamoKAgubi4KDY2Nk/Henh4yGKx5Gj38vLK7zBzlZqaqqioKHXr1i3HPnd391yPGTNmjEaMGGHfttlsCmtQt8BiBFA85fXfQQD5x2azycfHx+wwrotcAgAAZEeRKp94eXkpJCTEoS00NFQZGRnauHGj/XG/kydPKjk5Wffee+8tzxkaGprjF8Aff/zRYbtatWravHmzQ1v27bp16yo5OTlH/NditVpltVpvMGIAAIDLyCUAAEB2LJxegKpUqaLOnTurb9++WrdunXbs2KEnn3xS5cqVU+fOnW95/P79+yslJUUvvviikpOTtWjRIsXExDj0GTJkiFasWKEZM2YoJSVF8+bN09dff+1w59a4ceP0v//9T1FRUfrll1+UlJSkxYsX69VXX73lGAEAAAAAAPKCIlUBi46OVr169dShQwc1atRIhmFoxYoVcnV1veWxK1SooM8//1zLly9XrVq19O6772rSpEkOfZo0aaJ3331XM2bMUK1atbRy5Uq98MILDo/xtWvXTnFxcVq9erXuv/9+PfDAA/rPf/6j4ODgW44RAAAAAAAgLyyGYRhmB4HC1bdvX+3Zs0c//PBDvo1ps9kUXD3vjwsWVZYHb64w13lA75ue888zy6/bp2O143kaq+m9u246Dlxb9U9WmR1CkXe+VNccbaVanzMhEuDOdmVNqjNnzsjb29vscPLsdsklioObzXduxq3kSDcqLzlVXuQ177oe8jKYjfwVV5Nb3p5d6TbnTcklWJPqDjBt2jS1adNGXl5e+vrrr/XBBx9ozpw5+T7P30eOF7uEuGgYkn9DUXKGiayiIAXg1pBL4NbkY06VH8jLABRR18vbbTabJHO+hIUi1R1g06ZNevPNN3X27FlVrlxZs2bN0nPPPWd2WAAAAAAAAHYUqe4An3zyidkhAAAAAAAAXBMLpwMAAAAAAMB0FKkAAAAAAABgOopUAAAAAAAAMB1FKgAAAAAAAJiOIhUAAAAAAABMx7f7Id+U6lFfFZ9r5dB2/xPeJkXj6M2ua8wOIc9q/fFazsagsYUfCAAAhSy3XKKgFJUcBSgsxSkfBpC7XH9XLABmfkJyJ1UuIiMj1aVLF/t28+bNNXz48FsaMyYmRr6+vrc0BgAAAAAAwO3qtipSRUZGymKx2F9+fn4KDw/Xzp07b2icmTNnKiYmpmCCLCQHDx6UxWLR9u3bzQ4FAAAAAADgum6rIpUkhYeH68iRIzpy5Iji4+Pl4uKiDh063NAYPj4+3PX0D+np6WaHAAAAAAAAbnO3XZHKarUqICBAAQEBql27tkaPHq3Dhw/r+PHj9j67du1Sy5Yt5eHhIT8/P/Xr10+pqan2/dkf98suLS1No0aNUrly5eTl5aWGDRsqISHBoU9MTIwqVKggT09Pde3aVSdPnrxm3I8++qgGDx5s3x4+fLgsFov27NkjSbp06ZK8vLz07bffSpJWrlypBx98UL6+vvLz81OHDh20b98++/GVKlWSJNWpU0cWi0XNmze371uwYIFCQ0Pl7u6u6tWra86cOfZ9V+7AWrJkiZo1ayZ3d3ctXLjwmrEDAAAAAADcqtuuSPVPqamp+uijjxQSEiI/Pz9J0rlz59SuXTuVKlVKmzdv1qeffqpvv/3WoUB0PYMHD9aGDRu0ePFi7dy5Uz169FB4eLhSUlIkSRs3btSzzz6rwYMHa/v27WrRooXeeOONa47ZrFkzh0LXd999pzJlytjbNm/erPT0dDVu3Nh+HiNGjNCWLVsUHx8vJycnde3aVVlZWZKkTZs2SZK+/fZbHTlyREuXLpUkLVy4UOPGjdPEiROVlJSkSZMmaezYsfrggw8c4hk9erSGDRumpKQktWvXLke8aWlpstlsDi8AAIC8IpcAAADZ3Xbf7hcXF6cSJUpIulzICQwMVFxcnJycLtfjFi1apIsXL+p///ufvLy8JEmzZ89Wx44dNWXKFJUtW/aa4x86dEjR0dE6dOiQgoKCJEmjRo3SypUrFR0drUmTJmnmzJkKDw/XSy+9JEmqWrWq1q9fr5UrV1513ObNm2vYsGE6fvy4XFxctHv3bo0dO1YJCQnq37+/EhISdP/998vT01OS1L17d4fj33//ffn7+2v37t2qUaOG/P39JUl+fn4KCAiw9xs/frymT5+ubt26Sbp8x9Xu3bs1b9489enTx95v+PDh9j65mTx5sqKiohzaSvWof833DjevU6dOZocAACgmistj+uQSAAAgu9uuSNW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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "process_and_visualize_meals(\n",
        "    breakfast_count, breakfast_percentage,\n",
        "    lunch_count, lunch_percentage,\n",
        "    dinner_count, dinner_percentage,\n",
        "    snacks_count, snacks_percentage\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P5JCMRcMQ954"
      },
      "source": [
        "## Appliance use by connection type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u-uybLcRx90W"
      },
      "outputs": [],
      "source": [
        "# Create a temporary DataFrame for calculations\n",
        "df_temp = df_cooking_survey['Spotlight Kampala Cooking Su...'].copy()\n",
        "\n",
        "# Define meal types and specific connection types\n",
        "meal_types = ['breakfast', 'lunch', 'dinner', 'snacks']\n",
        "connection_types = [2, 5, 6]  # Specific connection types to calculate\n",
        "sa_no = len(connection_types)  # Number of connection types to model\n",
        "\n",
        "# Function to calculate probabilities\n",
        "def calculate_prob(df, column, connections, fill_na=False, normalization_factor=1):\n",
        "    \"\"\"\n",
        "    Calculate probability based on a specified data column and specific connection types.\n",
        "\n",
        "    Args:\n",
        "    df (pd.DataFrame): DataFrame containing the data.\n",
        "    column (str): Column name for data.\n",
        "    connections (list): List of connection types to calculate probabilities for.\n",
        "    fill_na (bool): Whether to set null values to 0.\n",
        "    normalization_factor (int): Normalization factor to convert frequency data into daily rates, defaults to 1.\n",
        "\n",
        "    Returns:\n",
        "    dict: A dictionary containing probability for each connection type.\n",
        "    \"\"\"\n",
        "    prob_calc = {}\n",
        "    if fill_na:\n",
        "        # Avoid inplace modification; assign filled values back to the column\n",
        "        df[column] = df[column].fillna(0)\n",
        "\n",
        "    for connection in connections:\n",
        "        filtered_data = df[df['Connection type'] == connection][column] / normalization_factor\n",
        "        prob_calc[connection] = np.mean(filtered_data)\n",
        "\n",
        "    return prob_calc\n",
        "\n",
        "# Initialize dictionaries for results\n",
        "probability_cooks_at_least_once = {}\n",
        "average_days_cooking_per_week = {}\n",
        "daily_probabilities = {}\n",
        "\n",
        "# Calculate the probability of preparing each meal\n",
        "for meal in meal_types:\n",
        "    column_name = f'Which of the following meals are prepared at least once a week for members of the household?/{meal.capitalize()}'\n",
        "    if meal == 'snacks':\n",
        "        # Assign a probability of 1 for all specified connection types for snacks\n",
        "        probability_cooks_at_least_once[meal] = {connection: 1 for connection in connection_types}\n",
        "    else:\n",
        "        # For other meals, calculate based on data\n",
        "        fill_na = False\n",
        "        probabilities = calculate_prob(df_temp, column_name, connection_types, fill_na=fill_na)\n",
        "        probability_cooks_at_least_once[meal] = probabilities\n",
        "\n",
        "# Calculate the frequency of cooking for those who do\n",
        "for meal in meal_types:\n",
        "    column_name = f'How many times a week is {meal} prepared at home?'\n",
        "    # Apply fill_na=True only for snacks\n",
        "    fill_na = True if meal == 'snacks' else False\n",
        "    average_days = calculate_prob(df_temp, column_name, connection_types, fill_na=fill_na)\n",
        "    average_days_cooking_per_week[meal] = average_days\n",
        "\n",
        "# Calculate daily probabilities\n",
        "for meal, probabilities in probability_cooks_at_least_once.items():\n",
        "    daily_probabilities[meal] = {}\n",
        "    for connection, probability in probabilities.items():\n",
        "        if average_days_cooking_per_week[meal].get(connection) is not None:\n",
        "            daily_prob = probability * average_days_cooking_per_week[meal][connection] / 7\n",
        "        else:\n",
        "            daily_prob = 0  # Default to 0 if NaN\n",
        "        daily_probabilities[meal][connection] = daily_prob\n",
        "\n",
        "# Display results\n",
        "results_df = pd.DataFrame(daily_probabilities)\n",
        "meal_probabilities = results_df.to_dict()  # Convert probabilities to a dictionary for easy lookup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iWPr8BA1F7DC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "d07b7c6f-011a-4f18-cc5a-07afaa027ebe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of unique survey responses included: 120\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Group appliances by fuel source for the legend\n",
        "grouped_labels = {}\n",
        "for appliance, fuel in appliance_fuel_source.items():\n",
        "    if fuel not in grouped_labels:\n",
        "        grouped_labels[fuel] = []\n",
        "    grouped_labels[fuel].append(appliance)\n",
        "\n",
        "# Define a function to process and weight meal data\n",
        "def process_meal_weighted(df_meal, question_col, meal_name, survey_df, meal_probabilities):\n",
        "    \"\"\"\n",
        "    Processes a meal DataFrame to extract appliance usage, add connection type, and weight by probabilities.\n",
        "\n",
        "    Args:\n",
        "        df_meal (DataFrame): Meal-specific data.\n",
        "        question_col (str): Column name for the cooking method question.\n",
        "        meal_name (str): Name of the meal (e.g., 'breakfast').\n",
        "        survey_df (DataFrame): Main survey DataFrame containing '_id' and 'Connection type'.\n",
        "        meal_probabilities (dict): Dictionary of probabilities for each meal by connection type.\n",
        "\n",
        "    Returns:\n",
        "        DataFrame: DataFrame containing weighted appliance counts for the given meal.\n",
        "    \"\"\"\n",
        "    # Select relevant columns and filter by appliance list\n",
        "    df = df_meal[['_submission__id', question_col]].rename(columns={question_col: 'Appliance'})\n",
        "    df = df[df['Appliance'].isin(appliance_list)]\n",
        "\n",
        "    # Add the meal name as a column\n",
        "    df['Meal'] = meal_name\n",
        "\n",
        "    # Map '_submission__id' to 'Connection type'\n",
        "    connection_map = df_temp.set_index('_id')['Connection type']\n",
        "    df['Connection type'] = df['_submission__id'].map(connection_map)\n",
        "\n",
        "\n",
        "    # Count appliances by connection type\n",
        "    appliance_counts = df.groupby(['Connection type', 'Appliance']).size().reset_index(name='Count')\n",
        "\n",
        "    # Add meal probabilities to the counts\n",
        "    appliance_counts['Probability'] = appliance_counts['Connection type'].map(\n",
        "        lambda x: meal_probabilities[meal_name].get(x, 0)\n",
        "    )\n",
        "\n",
        "    # Weight counts by meal probability\n",
        "    appliance_counts['Weighted Count'] = appliance_counts['Count'] * appliance_counts['Probability']\n",
        "\n",
        "    return appliance_counts[['Connection type', 'Appliance', 'Weighted Count']]\n",
        "\n",
        "# Collect unique submission IDs across all meals\n",
        "all_submission_ids = pd.concat([\n",
        "    df_breakfast[['_submission__id']],\n",
        "    df_lunch[['_submission__id']],\n",
        "    df_dinner[['_submission__id']],\n",
        "    df_snacks[['_submission__id']]\n",
        "], ignore_index=True)\n",
        "\n",
        "# Drop duplicates and count\n",
        "unique_respondents = all_submission_ids['_submission__id'].nunique()\n",
        "print(f\"Number of unique survey responses included: {unique_respondents}\")\n",
        "\n",
        "# Process each meal DataFrame and calculate weighted counts\n",
        "meals_data = []\n",
        "for meal_name, df_meal, question_col in [\n",
        "    ('breakfast', df_breakfast, 'When you prepare ${breakfast_home_list} at home, how do you most often cook it?'),\n",
        "    ('lunch', df_lunch, 'When you prepare ${lunch_home_list} at home, how do you most often cook it?'),\n",
        "    ('dinner', df_dinner, 'When you prepare ${dinner_home_list} at home, how do you most often cook it?'),\n",
        "    ('snacks', df_snacks, 'When you prepare ${snacks_home_list} at home, how do you most often cook it?')\n",
        "]:\n",
        "    meals_data.append(\n",
        "        process_meal_weighted(df_meal, question_col, meal_name, df_cooking_survey, meal_probabilities)\n",
        "    )\n",
        "\n",
        "# Combine weighted counts from all meals\n",
        "all_meals_data = pd.concat(meals_data, ignore_index=True)\n",
        "\n",
        "# Aggregate weighted counts across meals for each connection type and appliance\n",
        "aggregated_counts = (\n",
        "    all_meals_data\n",
        "    .groupby(['Connection type', 'Appliance'])['Weighted Count']\n",
        "    .sum()\n",
        "    .reset_index()\n",
        ")\n",
        "\n",
        "# Pivot for crosstab-like structure\n",
        "appliance_counts = aggregated_counts.pivot(\n",
        "    index='Connection type', columns='Appliance', values='Weighted Count'\n",
        ").fillna(0)\n",
        "\n",
        "# Convert counts to percentages for a 100% stacked bar chart\n",
        "appliance_percentages = appliance_counts.div(appliance_counts.sum(axis=1), axis=0) * 100\n",
        "\n",
        "# Ensure the bar stacks are ordered in the same way as the legend\n",
        "ordered_appliances = [appliance for group in grouped_labels.values() for appliance in group if appliance in appliance_percentages.columns]\n",
        "appliance_percentages = appliance_percentages[ordered_appliances]\n",
        "\n",
        "# Ensure all connection types are present\n",
        "available_types = [6, 5, 2]\n",
        "appliance_percentages = appliance_percentages.reindex(available_types, fill_value=0)\n",
        "\n",
        "# Map connection types to categorical labels\n",
        "appliance_percentages.index = ['Collective unmetered', 'Collective metered', 'Individual metered']\n",
        "\n",
        "# Plot the 100% stacked horizontal bar chart\n",
        "fig, ax = plt.subplots(figsize=(10, 4))\n",
        "\n",
        "# Initialize the left positions of the stacked bars\n",
        "left = np.zeros(len(appliance_percentages))\n",
        "\n",
        "# Plot each appliance as a stacked segment with custom colors\n",
        "for appliance in appliance_percentages.columns:\n",
        "    ax.barh(\n",
        "        appliance_percentages.index,  # Updated y-axis labels\n",
        "        appliance_percentages[appliance],  # Percentage values for the appliance\n",
        "        left=left,  # Start stacking from the previous total\n",
        "        color=appliance_colors[appliance],  # Use custom colors for the appliances\n",
        "        height=0.8  # Remove spaces between bars by reducing bar height\n",
        "    )\n",
        "    left += appliance_percentages[appliance]  # Update the left positions for the next appliance\n",
        "\n",
        "# Customize the plot\n",
        "ax.set_xlabel(\"Appliance by percentage of meals cooked\", fontsize=14)\n",
        "ax.set_ylabel(None)  # Remove y-axis label\n",
        "ax.set_xticks(range(0, 101, 10))\n",
        "ax.set_xticklabels([f\"{i}%\" for i in range(0, 101, 10)], fontsize=12)\n",
        "ax.tick_params(axis='y', labelsize=14)\n",
        "ax.tick_params(axis='x', labelsize=14)\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "ax.spines['left'].set_visible(False)\n",
        "ax.spines['bottom'].set_linewidth(0.8)\n",
        "\n",
        "# Remove gridlines\n",
        "ax.grid(False)\n",
        "\n",
        "# Add thin black border around the plot area\n",
        "for spine in ax.spines.values():\n",
        "    spine.set_visible(True)\n",
        "    spine.set_linewidth(0.8)\n",
        "    spine.set_edgecolor('black')\n",
        "\n",
        "# Adjust layout to avoid overlap\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig(fig_path + \"Appliance Use by Connection Type.png\", dpi=500)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NL8eGFUZZ9SS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "outputId": "6d98f252-bfdb-4664-a60c-fdbb1ce980c6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create the legend separately\n",
        "fig_legend, ax_legend = plt.subplots(figsize=(12, 4))\n",
        "handles = []\n",
        "labels = []\n",
        "\n",
        "for fuel, appliances in grouped_labels.items():\n",
        "    # Add a placeholder for fuel source headers\n",
        "    handles.append((plt.Line2D([0], [0], color='none'),))\n",
        "    labels.append(fuel)\n",
        "    # Add appliances under each fuel source\n",
        "    for appliance in appliances:\n",
        "        if appliance in appliance_percentages.columns:\n",
        "            handles.append(plt.Line2D([0], [0], color=appliance_colors[appliance], lw=10, markeredgewidth=0.5, markeredgecolor='black'))\n",
        "            labels.append(appliance_fuel_source_labels[appliance])\n",
        "\n",
        "# Add the legend to a separate figure\n",
        "fig_legend.legend(handles, labels, loc='upper left', frameon=False, fontsize=12, title=\"Cooking appliance\", title_fontsize=14)\n",
        "plt.axis('off')  # Remove axes for the legend-only plot\n",
        "\n",
        "# Show the legend\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sQ-oZG6YQ4h8"
      },
      "source": [
        "## Appliance use by fuel source"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aA1xhGJ18WYW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99
        },
        "outputId": "8037fa76-6cba-4b71-e48c-a0852f8f74fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recalculated meal preparation probabilities across the entire population:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   breakfast     lunch    dinner    snacks\n",
              "0   0.786905  0.745238  0.588095  0.109524"
            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"meal_probabilities_population = daily_probabilities_population\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"breakfast\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.7869047619047619,\n        \"max\": 0.7869047619047619,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.7869047619047619\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lunch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.7452380952380953,\n        \"max\": 0.7452380952380953,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.7452380952380953\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dinner\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.5880952380952381,\n        \"max\": 0.5880952380952381,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.5880952380952381\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"snacks\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.10952380952380954,\n        \"max\": 0.10952380952380954,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.10952380952380954\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define meal types\n",
        "meal_types = ['breakfast', 'lunch', 'dinner', 'snacks']\n",
        "\n",
        "# Function to calculate probabilities across the entire population\n",
        "def calculate_prob_population(df, column, fill_na=False, normalization_factor=1):\n",
        "    \"\"\"\n",
        "    Calculate probabilities across the entire population.\n",
        "\n",
        "    Args:\n",
        "    df (pd.DataFrame): DataFrame containing the data.\n",
        "    column (str): Column name for data.\n",
        "    fill_na (bool): Whether to set null values to 0.\n",
        "    normalization_factor (int): Normalization factor to convert frequency data into daily rates, defaults to 1.\n",
        "\n",
        "    Returns:\n",
        "    float: Probability of the given meal being prepared.\n",
        "    \"\"\"\n",
        "    if fill_na:\n",
        "        df[column] = df[column].fillna(0)\n",
        "    filtered_data = df[column] / normalization_factor\n",
        "    return np.mean(filtered_data)\n",
        "\n",
        "# Initialize dictionaries for results\n",
        "probability_cooks_at_least_once = {}\n",
        "average_days_cooking_per_week = {}\n",
        "\n",
        "# Calculate the probability of preparing each meal across the entire population\n",
        "for meal in meal_types:\n",
        "    column_name = f'Which of the following meals are prepared at least once a week for members of the household?/{meal.capitalize()}'\n",
        "    if meal == 'snacks':\n",
        "        # Assign a fixed probability for snacks\n",
        "        probability_cooks_at_least_once[meal] = 1  # Snacks always prepared\n",
        "    else:\n",
        "        # Calculate probabilities based on data\n",
        "        fill_na = False\n",
        "        probabilities = calculate_prob_population(df_temp, column_name, fill_na=fill_na)\n",
        "        probability_cooks_at_least_once[meal] = probabilities\n",
        "\n",
        "# Calculate the frequency of cooking for those who do\n",
        "for meal in meal_types:\n",
        "    column_name = f'How many times a week is {meal} prepared at home?'\n",
        "    # Apply fill_na=True only for snacks\n",
        "    fill_na = True if meal == 'snacks' else False\n",
        "    average_days = calculate_prob_population(df_temp, column_name, fill_na=fill_na)\n",
        "    average_days_cooking_per_week[meal] = average_days\n",
        "\n",
        "# Calculate daily probabilities for the entire population\n",
        "daily_probabilities_population = {}\n",
        "for meal, probability in probability_cooks_at_least_once.items():\n",
        "    if average_days_cooking_per_week[meal] is not None:\n",
        "        daily_prob = probability * average_days_cooking_per_week[meal] / 7\n",
        "    else:\n",
        "        daily_prob = 0  # Default to 0 if NaN\n",
        "    daily_probabilities_population[meal] = daily_prob\n",
        "\n",
        "# Display recalculated probabilities\n",
        "print(\"Recalculated meal preparation probabilities across the entire population:\")\n",
        "display(pd.DataFrame(daily_probabilities_population, index=[0]))\n",
        "\n",
        "# Convert to a dictionary for easy lookup\n",
        "meal_probabilities_population = daily_probabilities_population"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NmX5p8Jr6hXB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "663cd4f7-5c60-420a-9db8-ae9275f03b6d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updated Weighted Counts:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                      Appliance       Meal  Count  \\\n",
              "0                                 Cooking coils  breakfast      6   \n",
              "1                                 Cooking coils     dinner     37   \n",
              "2                                 Cooking coils      lunch     49   \n",
              "3                                 Cooking coils     snacks      3   \n",
              "4                         Deep fryer (electric)     dinner      2   \n",
              "5                         Deep fryer (electric)     snacks      1   \n",
              "6                                     Hot plate  breakfast     21   \n",
              "7                                     Hot plate     dinner     80   \n",
              "8                                     Hot plate      lunch     52   \n",
              "9                                     Hot plate     snacks     29   \n",
              "10                              Oven (electric)     dinner      2   \n",
              "11                              Oven (electric)     snacks      1   \n",
              "12                                   Oven (gas)      lunch      1   \n",
              "13                                   Percolator  breakfast     42   \n",
              "14                                   Percolator     snacks      8   \n",
              "15                   Pressure cooker (charcoal)     dinner      2   \n",
              "16                   Pressure cooker (charcoal)      lunch      4   \n",
              "17                   Pressure cooker (electric)  breakfast      6   \n",
              "18                   Pressure cooker (electric)     dinner     18   \n",
              "19                   Pressure cooker (electric)      lunch     28   \n",
              "20                   Pressure cooker (electric)     snacks      3   \n",
              "21                        Pressure cooker (gas)  breakfast      2   \n",
              "22                        Pressure cooker (gas)      lunch      1   \n",
              "23                                  Rice cooker     dinner      5   \n",
              "24                                  Rice cooker      lunch      6   \n",
              "25           Sigiri (high-efficiency, charcoal)  breakfast     26   \n",
              "26           Sigiri (high-efficiency, charcoal)     dinner     64   \n",
              "27           Sigiri (high-efficiency, charcoal)      lunch    124   \n",
              "28           Sigiri (high-efficiency, charcoal)     snacks      7   \n",
              "29  Sigiri (high-efficiency, energy briquettes)     snacks      1   \n",
              "30               Sigiri (traditional, charcoal)  breakfast     59   \n",
              "31               Sigiri (traditional, charcoal)     dinner    257   \n",
              "32               Sigiri (traditional, charcoal)      lunch    341   \n",
              "33               Sigiri (traditional, charcoal)     snacks      2   \n",
              "34      Sigiri (traditional, energy briquettes)      lunch      2   \n",
              "35                            Stones (firewood)     dinner      6   \n",
              "36                            Stones (firewood)      lunch      8   \n",
              "37                             Stove (electric)  breakfast      3   \n",
              "38                             Stove (electric)     dinner      5   \n",
              "39                             Stove (electric)      lunch      1   \n",
              "40                                  Stove (gas)  breakfast      1   \n",
              "41                                  Stove (gas)     dinner      1   \n",
              "42                                  Stove (gas)      lunch      4   \n",
              "43                                  Stove (gas)     snacks      2   \n",
              "44                             Stove (kerosene)     snacks      1   \n",
              "\n",
              "    Fuel Source  Weighted Count  \n",
              "0   Electricity        4.721429  \n",
              "1   Electricity       21.759524  \n",
              "2   Electricity       36.516667  \n",
              "3   Electricity        0.328571  \n",
              "4   Electricity        1.176190  \n",
              "5   Electricity        0.109524  \n",
              "6   Electricity       16.525000  \n",
              "7   Electricity       47.047619  \n",
              "8   Electricity       38.752381  \n",
              "9   Electricity        3.176190  \n",
              "10  Electricity        1.176190  \n",
              "11  Electricity        0.109524  \n",
              "12          Gas        0.745238  \n",
              "13  Electricity       33.050000  \n",
              "14  Electricity        0.876190  \n",
              "15     Charcoal        1.176190  \n",
              "16     Charcoal        2.980952  \n",
              "17  Electricity        4.721429  \n",
              "18  Electricity       10.585714  \n",
              "19  Electricity       20.866667  \n",
              "20  Electricity        0.328571  \n",
              "21          Gas        1.573810  \n",
              "22          Gas        0.745238  \n",
              "23  Electricity        2.940476  \n",
              "24  Electricity        4.471429  \n",
              "25     Charcoal       20.459524  \n",
              "26     Charcoal       37.638095  \n",
              "27     Charcoal       92.409524  \n",
              "28     Charcoal        0.766667  \n",
              "29        Other        0.109524  \n",
              "30     Charcoal       46.427381  \n",
              "31     Charcoal      151.140476  \n",
              "32     Charcoal      254.126190  \n",
              "33     Charcoal        0.219048  \n",
              "34        Other        1.490476  \n",
              "35     Firewood        3.528571  \n",
              "36     Firewood        5.961905  \n",
              "37  Electricity        2.360714  \n",
              "38  Electricity        2.940476  \n",
              "39  Electricity        0.745238  \n",
              "40          Gas        0.786905  \n",
              "41          Gas        0.588095  \n",
              "42          Gas        2.980952  \n",
              "43          Gas        0.219048  \n",
              "44        Other        0.109524  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Appliance</th>\n",
              "      <th>Meal</th>\n",
              "      <th>Count</th>\n",
              "      <th>Fuel Source</th>\n",
              "      <th>Weighted Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Cooking coils</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>6</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>4.721429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cooking coils</td>\n",
              "      <td>dinner</td>\n",
              "      <td>37</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>21.759524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cooking coils</td>\n",
              "      <td>lunch</td>\n",
              "      <td>49</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>36.516667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cooking coils</td>\n",
              "      <td>snacks</td>\n",
              "      <td>3</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>0.328571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Deep fryer (electric)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>1.176190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Deep fryer (electric)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>1</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>0.109524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Hot plate</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>21</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>16.525000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Hot plate</td>\n",
              "      <td>dinner</td>\n",
              "      <td>80</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>47.047619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Hot plate</td>\n",
              "      <td>lunch</td>\n",
              "      <td>52</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>38.752381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Hot plate</td>\n",
              "      <td>snacks</td>\n",
              "      <td>29</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>3.176190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Oven (electric)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>1.176190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Oven (electric)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>1</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>0.109524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Oven (gas)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>1</td>\n",
              "      <td>Gas</td>\n",
              "      <td>0.745238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Percolator</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>42</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>33.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Percolator</td>\n",
              "      <td>snacks</td>\n",
              "      <td>8</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>0.876190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Pressure cooker (charcoal)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>1.176190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Pressure cooker (charcoal)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>4</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>2.980952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Pressure cooker (electric)</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>6</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>4.721429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Pressure cooker (electric)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>18</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>10.585714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Pressure cooker (electric)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>28</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>20.866667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Pressure cooker (electric)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>3</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>0.328571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Pressure cooker (gas)</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>2</td>\n",
              "      <td>Gas</td>\n",
              "      <td>1.573810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Pressure cooker (gas)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>1</td>\n",
              "      <td>Gas</td>\n",
              "      <td>0.745238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Rice cooker</td>\n",
              "      <td>dinner</td>\n",
              "      <td>5</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>2.940476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Rice cooker</td>\n",
              "      <td>lunch</td>\n",
              "      <td>6</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>4.471429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Sigiri (high-efficiency, charcoal)</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>26</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>20.459524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Sigiri (high-efficiency, charcoal)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>64</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>37.638095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Sigiri (high-efficiency, charcoal)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>124</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>92.409524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Sigiri (high-efficiency, charcoal)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>7</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>0.766667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Sigiri (high-efficiency, energy briquettes)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>1</td>\n",
              "      <td>Other</td>\n",
              "      <td>0.109524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Sigiri (traditional, charcoal)</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>59</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>46.427381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Sigiri (traditional, charcoal)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>257</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>151.140476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Sigiri (traditional, charcoal)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>341</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>254.126190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Sigiri (traditional, charcoal)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>2</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>0.219048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Sigiri (traditional, energy briquettes)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>2</td>\n",
              "      <td>Other</td>\n",
              "      <td>1.490476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Stones (firewood)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>6</td>\n",
              "      <td>Firewood</td>\n",
              "      <td>3.528571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Stones (firewood)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>8</td>\n",
              "      <td>Firewood</td>\n",
              "      <td>5.961905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Stove (electric)</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>3</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>2.360714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Stove (electric)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>5</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>2.940476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Stove (electric)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>1</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>0.745238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Stove (gas)</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>1</td>\n",
              "      <td>Gas</td>\n",
              "      <td>0.786905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Stove (gas)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>1</td>\n",
              "      <td>Gas</td>\n",
              "      <td>0.588095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Stove (gas)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>4</td>\n",
              "      <td>Gas</td>\n",
              "      <td>2.980952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Stove (gas)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>2</td>\n",
              "      <td>Gas</td>\n",
              "      <td>0.219048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Stove (kerosene)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>1</td>\n",
              "      <td>Other</td>\n",
              "      <td>0.109524</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    .colab-df-convert:hover {\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "      spin 1s steps(1) infinite;\n",
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              "  @keyframes spin {\n",
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              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
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              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"print(f\\\"n = {unique_ids['_submission__id']\",\n  \"rows\": 45,\n  \"fields\": [\n    {\n      \"column\": \"Appliance\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"Cooking coils\",\n          \"Deep fryer (electric)\",\n          \"Pressure cooker (gas)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Meal\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"dinner\",\n          \"snacks\",\n          \"breakfast\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64,\n        \"min\": 1,\n        \"max\": 341,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          5,\n          29,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuel Source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Gas\",\n          \"Firewood\",\n          \"Charcoal\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weighted Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45.30174788327746,\n        \"min\": 0.10952380952380954,\n        \"max\": 254.1261904761905,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          0.7869047619047619,\n          20.866666666666667,\n          0.21904761904761907\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "data": {
            "text/plain": [
              "                                      Appliance       Meal  Count  \\\n",
              "0                                 Cooking coils  breakfast      6   \n",
              "1                                 Cooking coils     dinner     37   \n",
              "2                                 Cooking coils      lunch     49   \n",
              "3                                 Cooking coils     snacks      3   \n",
              "4                         Deep fryer (electric)     dinner      2   \n",
              "5                         Deep fryer (electric)     snacks      1   \n",
              "6                                     Hot plate  breakfast     21   \n",
              "7                                     Hot plate     dinner     80   \n",
              "8                                     Hot plate      lunch     52   \n",
              "9                                     Hot plate     snacks     29   \n",
              "10                              Oven (electric)     dinner      2   \n",
              "11                              Oven (electric)     snacks      1   \n",
              "12                                   Oven (gas)      lunch      1   \n",
              "13                                   Percolator  breakfast     42   \n",
              "14                                   Percolator     snacks      8   \n",
              "15                   Pressure cooker (charcoal)     dinner      2   \n",
              "16                   Pressure cooker (charcoal)      lunch      4   \n",
              "17                   Pressure cooker (electric)  breakfast      6   \n",
              "18                   Pressure cooker (electric)     dinner     18   \n",
              "19                   Pressure cooker (electric)      lunch     28   \n",
              "20                   Pressure cooker (electric)     snacks      3   \n",
              "21                        Pressure cooker (gas)  breakfast      2   \n",
              "22                        Pressure cooker (gas)      lunch      1   \n",
              "23                                  Rice cooker     dinner      5   \n",
              "24                                  Rice cooker      lunch      6   \n",
              "25           Sigiri (high-efficiency, charcoal)  breakfast     26   \n",
              "26           Sigiri (high-efficiency, charcoal)     dinner     64   \n",
              "27           Sigiri (high-efficiency, charcoal)      lunch    124   \n",
              "28           Sigiri (high-efficiency, charcoal)     snacks      7   \n",
              "29  Sigiri (high-efficiency, energy briquettes)     snacks      1   \n",
              "30               Sigiri (traditional, charcoal)  breakfast     59   \n",
              "31               Sigiri (traditional, charcoal)     dinner    257   \n",
              "32               Sigiri (traditional, charcoal)      lunch    341   \n",
              "33               Sigiri (traditional, charcoal)     snacks      2   \n",
              "34      Sigiri (traditional, energy briquettes)      lunch      2   \n",
              "35                            Stones (firewood)     dinner      6   \n",
              "36                            Stones (firewood)      lunch      8   \n",
              "37                             Stove (electric)  breakfast      3   \n",
              "38                             Stove (electric)     dinner      5   \n",
              "39                             Stove (electric)      lunch      1   \n",
              "40                                  Stove (gas)  breakfast      1   \n",
              "41                                  Stove (gas)     dinner      1   \n",
              "42                                  Stove (gas)      lunch      4   \n",
              "43                                  Stove (gas)     snacks      2   \n",
              "44                             Stove (kerosene)     snacks      1   \n",
              "\n",
              "    Fuel Source  Weighted Count  \n",
              "0   Electricity        4.721429  \n",
              "1   Electricity       21.759524  \n",
              "2   Electricity       36.516667  \n",
              "3   Electricity        0.328571  \n",
              "4   Electricity        1.176190  \n",
              "5   Electricity        0.109524  \n",
              "6   Electricity       16.525000  \n",
              "7   Electricity       47.047619  \n",
              "8   Electricity       38.752381  \n",
              "9   Electricity        3.176190  \n",
              "10  Electricity        1.176190  \n",
              "11  Electricity        0.109524  \n",
              "12          Gas        0.745238  \n",
              "13  Electricity       33.050000  \n",
              "14  Electricity        0.876190  \n",
              "15     Charcoal        1.176190  \n",
              "16     Charcoal        2.980952  \n",
              "17  Electricity        4.721429  \n",
              "18  Electricity       10.585714  \n",
              "19  Electricity       20.866667  \n",
              "20  Electricity        0.328571  \n",
              "21          Gas        1.573810  \n",
              "22          Gas        0.745238  \n",
              "23  Electricity        2.940476  \n",
              "24  Electricity        4.471429  \n",
              "25     Charcoal       20.459524  \n",
              "26     Charcoal       37.638095  \n",
              "27     Charcoal       92.409524  \n",
              "28     Charcoal        0.766667  \n",
              "29        Other        0.109524  \n",
              "30     Charcoal       46.427381  \n",
              "31     Charcoal      151.140476  \n",
              "32     Charcoal      254.126190  \n",
              "33     Charcoal        0.219048  \n",
              "34        Other        1.490476  \n",
              "35     Firewood        3.528571  \n",
              "36     Firewood        5.961905  \n",
              "37  Electricity        2.360714  \n",
              "38  Electricity        2.940476  \n",
              "39  Electricity        0.745238  \n",
              "40          Gas        0.786905  \n",
              "41          Gas        0.588095  \n",
              "42          Gas        2.980952  \n",
              "43          Gas        0.219048  \n",
              "44        Other        0.109524  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Appliance</th>\n",
              "      <th>Meal</th>\n",
              "      <th>Count</th>\n",
              "      <th>Fuel Source</th>\n",
              "      <th>Weighted Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Cooking coils</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>6</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>4.721429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cooking coils</td>\n",
              "      <td>dinner</td>\n",
              "      <td>37</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>21.759524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cooking coils</td>\n",
              "      <td>lunch</td>\n",
              "      <td>49</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>36.516667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cooking coils</td>\n",
              "      <td>snacks</td>\n",
              "      <td>3</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>0.328571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Deep fryer (electric)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>1.176190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Deep fryer (electric)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>1</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>0.109524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Hot plate</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>21</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>16.525000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Hot plate</td>\n",
              "      <td>dinner</td>\n",
              "      <td>80</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>47.047619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Hot plate</td>\n",
              "      <td>lunch</td>\n",
              "      <td>52</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>38.752381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Hot plate</td>\n",
              "      <td>snacks</td>\n",
              "      <td>29</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>3.176190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Oven (electric)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>1.176190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Oven (electric)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>1</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>0.109524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Oven (gas)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>1</td>\n",
              "      <td>Gas</td>\n",
              "      <td>0.745238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Percolator</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>42</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>33.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Percolator</td>\n",
              "      <td>snacks</td>\n",
              "      <td>8</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>0.876190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Pressure cooker (charcoal)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>1.176190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Pressure cooker (charcoal)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>4</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>2.980952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Pressure cooker (electric)</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>6</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>4.721429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Pressure cooker (electric)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>18</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>10.585714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Pressure cooker (electric)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>28</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>20.866667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Pressure cooker (electric)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>3</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>0.328571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Pressure cooker (gas)</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>2</td>\n",
              "      <td>Gas</td>\n",
              "      <td>1.573810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Pressure cooker (gas)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>1</td>\n",
              "      <td>Gas</td>\n",
              "      <td>0.745238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Rice cooker</td>\n",
              "      <td>dinner</td>\n",
              "      <td>5</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>2.940476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Rice cooker</td>\n",
              "      <td>lunch</td>\n",
              "      <td>6</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>4.471429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Sigiri (high-efficiency, charcoal)</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>26</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>20.459524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Sigiri (high-efficiency, charcoal)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>64</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>37.638095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Sigiri (high-efficiency, charcoal)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>124</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>92.409524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Sigiri (high-efficiency, charcoal)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>7</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>0.766667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Sigiri (high-efficiency, energy briquettes)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>1</td>\n",
              "      <td>Other</td>\n",
              "      <td>0.109524</td>\n",
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              "      <th>30</th>\n",
              "      <td>Sigiri (traditional, charcoal)</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>59</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>46.427381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Sigiri (traditional, charcoal)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>257</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>151.140476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Sigiri (traditional, charcoal)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>341</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>254.126190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Sigiri (traditional, charcoal)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>2</td>\n",
              "      <td>Charcoal</td>\n",
              "      <td>0.219048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Sigiri (traditional, energy briquettes)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>2</td>\n",
              "      <td>Other</td>\n",
              "      <td>1.490476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Stones (firewood)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>6</td>\n",
              "      <td>Firewood</td>\n",
              "      <td>3.528571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Stones (firewood)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>8</td>\n",
              "      <td>Firewood</td>\n",
              "      <td>5.961905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Stove (electric)</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>3</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>2.360714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Stove (electric)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>5</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>2.940476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Stove (electric)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>1</td>\n",
              "      <td>Electricity</td>\n",
              "      <td>0.745238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Stove (gas)</td>\n",
              "      <td>breakfast</td>\n",
              "      <td>1</td>\n",
              "      <td>Gas</td>\n",
              "      <td>0.786905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Stove (gas)</td>\n",
              "      <td>dinner</td>\n",
              "      <td>1</td>\n",
              "      <td>Gas</td>\n",
              "      <td>0.588095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Stove (gas)</td>\n",
              "      <td>lunch</td>\n",
              "      <td>4</td>\n",
              "      <td>Gas</td>\n",
              "      <td>2.980952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Stove (gas)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>2</td>\n",
              "      <td>Gas</td>\n",
              "      <td>0.219048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Stove (kerosene)</td>\n",
              "      <td>snacks</td>\n",
              "      <td>1</td>\n",
              "      <td>Other</td>\n",
              "      <td>0.109524</td>\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-6aa2badb-147b-4cbb-9895-480c5b92f8de button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_c647f629-2e52-4667-829d-53d0e6e1ec8a\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('all_counts_long')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_c647f629-2e52-4667-829d-53d0e6e1ec8a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('all_counts_long');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "all_counts_long",
              "summary": "{\n  \"name\": \"all_counts_long\",\n  \"rows\": 45,\n  \"fields\": [\n    {\n      \"column\": \"Appliance\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"Cooking coils\",\n          \"Deep fryer (electric)\",\n          \"Pressure cooker (gas)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Meal\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"dinner\",\n          \"snacks\",\n          \"breakfast\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64,\n        \"min\": 1,\n        \"max\": 341,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          5,\n          29,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuel Source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Gas\",\n          \"Firewood\",\n          \"Charcoal\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weighted Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45.30174788327746,\n        \"min\": 0.10952380952380954,\n        \"max\": 254.1261904761905,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          0.7869047619047619,\n          20.866666666666667,\n          0.21904761904761907\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n = 120 unique respondents\n"
          ]
        }
      ],
      "source": [
        "# Combine raw counts for all meals\n",
        "all_counts = []\n",
        "for meal_name, df_meal, question_col in [\n",
        "    ('breakfast', df_breakfast, 'When you prepare ${breakfast_home_list} at home, how do you most often cook it?'),\n",
        "    ('lunch', df_lunch, 'When you prepare ${lunch_home_list} at home, how do you most often cook it?'),\n",
        "    ('dinner', df_dinner, 'When you prepare ${dinner_home_list} at home, how do you most often cook it?'),\n",
        "    ('snacks', df_snacks, 'When you prepare ${snacks_home_list} at home, how do you most often cook it?')\n",
        "]:\n",
        "    # Extract appliance and count for each meal\n",
        "    processed = df_meal[['_submission__id', question_col]].rename(columns={question_col: 'Appliance'})\n",
        "    processed['Meal'] = meal_name\n",
        "    all_counts.append(processed)\n",
        "\n",
        "# Concatenate data for all meals\n",
        "all_counts_long = pd.concat(all_counts, ignore_index=True)\n",
        "\n",
        "# Group by appliance and meal to get raw counts\n",
        "all_counts_long = (\n",
        "    all_counts_long.groupby(['Appliance', 'Meal'])\n",
        "    .size()\n",
        "    .reset_index(name='Count')\n",
        ")\n",
        "\n",
        "# Create 'Fuel Source' column in all_counts_long\n",
        "all_counts_long['Fuel Source'] = all_counts_long['Appliance'].map(appliance_fuel_source)\n",
        "\n",
        "# Update Weighted Count using recalculated probabilities\n",
        "all_counts_long['Weighted Count'] = all_counts_long.apply(\n",
        "    lambda row: row['Count'] * meal_probabilities_population.get(row['Meal'], 0),\n",
        "    axis=1\n",
        ")\n",
        "\n",
        "# Debug: Verify Weighted Count\n",
        "print(\"Updated Weighted Counts:\")\n",
        "display(all_counts_long[['Appliance', 'Meal', 'Count', 'Fuel Source', 'Weighted Count']])\n",
        "\n",
        "\n",
        "display(all_counts_long)\n",
        "\n",
        "# Count unique respondents across all meals\n",
        "unique_ids = pd.concat([df[['_submission__id']] for _, df, _ in [\n",
        "    ('breakfast', df_breakfast, 'When you prepare ${breakfast_home_list} at home, how do you most often cook it?'),\n",
        "    ('lunch', df_lunch, 'When you prepare ${lunch_home_list} at home, how do you most often cook it?'),\n",
        "    ('dinner', df_dinner, 'When you prepare ${dinner_home_list} at home, how do you most often cook it?'),\n",
        "    ('snacks', df_snacks, 'When you prepare ${snacks_home_list} at home, how do you most often cook it?')\n",
        "]]).drop_duplicates()\n",
        "\n",
        "print(f\"n = {unique_ids['_submission__id'].nunique()} unique respondents\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X-exprSKclk9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "outputId": "982da1fd-3fcf-4770-c8c6-910127263a1e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Aggregate counts by fuel source\n",
        "fuel_source_counts = all_counts_long.groupby('Fuel Source')['Weighted Count'].sum()\n",
        "\n",
        "# Handle missing data\n",
        "fuel_source_counts = fuel_source_counts.fillna(0)\n",
        "\n",
        "# Normalize fuel source counts to percentages for the inner pie\n",
        "fuel_source_percentages_ordered = (fuel_source_counts / fuel_source_counts.sum() * 100).tolist()\n",
        "fuel_source_order = fuel_source_counts.index.tolist()\n",
        "\n",
        "# Aggregate counts by appliance within each fuel source\n",
        "appliance_counts = all_counts_long.groupby(['Fuel Source', 'Appliance'])['Weighted Count'].sum()\n",
        "\n",
        "# Calculate appliance percentages for the outer pie\n",
        "appliance_data = []\n",
        "for fuel in fuel_source_order:\n",
        "    total_fuel_percentage = fuel_source_counts[fuel] / fuel_source_counts.sum() * 100\n",
        "    fuel_appliance_counts = appliance_counts.loc[fuel]\n",
        "    total_fuel_count = fuel_appliance_counts.sum()\n",
        "\n",
        "    # Calculate each appliance's percentage contribution to the fuel source\n",
        "    for appliance, count in fuel_appliance_counts.items():\n",
        "        appliance_percentage = (count / total_fuel_count) * total_fuel_percentage\n",
        "        appliance_data.append((fuel, appliance, appliance_percentage))\n",
        "\n",
        "# Sort appliance data by fuel source and descending percentage\n",
        "appliance_data.sort(key=lambda x: (x[0], -x[2]))\n",
        "\n",
        "# Extract sorted percentages and colors for the outer pie\n",
        "sorted_percentages = [item[2] for item in appliance_data]\n",
        "sorted_colors = [appliance_colors[item[1]] for item in appliance_data]\n",
        "\n",
        "inner_pie_colors = {\n",
        "    \"Electricity\": \"#177A17\",\n",
        "    \"Charcoal\": \"#b20404\",\n",
        "    \"Gas\": \"#e3c522\",\n",
        "    \"Firewood\": \"#98430b\",\n",
        "    \"Other\": \"#71b3ff\"\n",
        "}\n",
        "\n",
        "# Prepare inner pie colors in the correct order\n",
        "inner_colors = [inner_pie_colors[fuel] for fuel in fuel_source_order]\n",
        "\n",
        "# Define custom colors for the inner pie\n",
        "inner_pie_colors = {\n",
        "    \"Electricity\": \"#177A17\",\n",
        "    \"Charcoal\": \"#b20404\",\n",
        "    \"Gas\": \"#e3c522\",\n",
        "    \"Firewood\": \"#98430b\",\n",
        "    \"Other\": \"#71b3ff\"\n",
        "}\n",
        "\n",
        "# Plot the nested hollow pie chart\n",
        "fig, ax = plt.subplots(figsize=(10, 10))\n",
        "size = 0.3\n",
        "\n",
        "# Inner ring (fuel source) - sorted and aligned with custom colors\n",
        "ax.pie(\n",
        "    fuel_source_percentages_ordered,\n",
        "    radius=1 - size,\n",
        "    labels=None,  # Remove inner labels\n",
        "    colors=inner_colors,  # Apply custom inner pie colors\n",
        "    wedgeprops=dict(width=size, edgecolor='w')\n",
        ")\n",
        "\n",
        "# Outer ring (appliances) - aligned with the inner ring\n",
        "ax.pie(\n",
        "    sorted_percentages,\n",
        "    radius=1,\n",
        "    labels=None,  # Remove labels\n",
        "    colors=sorted_colors,\n",
        "    wedgeprops=dict(width=size, edgecolor='w')\n",
        ")\n",
        "\n",
        "# Add title and display the plot\n",
        "ax.set_title(\"Nested Pie Chart: Fuel Sources and Appliances\", fontsize=16)\n",
        "\n",
        "fig.savefig(fig_path + \"Fuel Source Composition.png\", dpi=500)\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RxJwQOCvaZoy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "1aa07e6f-6598-4125-ec8c-b0c32d822614"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fuel Source and Appliance Percentages:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "             Fuel Source %  Sigiri (traditional, charcoal)  \\\n",
              "Charcoal         68.901347                       51.268175   \n",
              "Electricity      28.961393                        0.000000   \n",
              "Firewood          1.076666                        0.000000   \n",
              "Gas               0.866654                        0.000000   \n",
              "Other             0.193940                        0.000000   \n",
              "\n",
              "             Sigiri (high-efficiency, charcoal)  Pressure cooker (charcoal)  \\\n",
              "Charcoal                              17.161557                    0.471615   \n",
              "Electricity                            0.000000                    0.000000   \n",
              "Firewood                               0.000000                    0.000000   \n",
              "Gas                                    0.000000                    0.000000   \n",
              "Other                                  0.000000                    0.000000   \n",
              "\n",
              "             Hot plate  Cooking coils  Pressure cooker (electric)  Percolator  \\\n",
              "Charcoal      0.000000       0.000000                    0.000000    0.000000   \n",
              "Electricity  11.968791       7.184165                    4.141085    3.848824   \n",
              "Firewood      0.000000       0.000000                    0.000000    0.000000   \n",
              "Gas           0.000000       0.000000                    0.000000    0.000000   \n",
              "Other         0.000000       0.000000                    0.000000    0.000000   \n",
              "\n",
              "             Rice cooker  Stove (electric)  Deep fryer (electric)  \\\n",
              "Charcoal        0.000000          0.000000                0.00000   \n",
              "Electricity     0.840858          0.685949                0.14586   \n",
              "Firewood        0.000000          0.000000                0.00000   \n",
              "Gas             0.000000          0.000000                0.00000   \n",
              "Other           0.000000          0.000000                0.00000   \n",
              "\n",
              "             Oven (electric)  Stones (firewood)  Stove (gas)  \\\n",
              "Charcoal             0.00000           0.000000      0.00000   \n",
              "Electricity          0.14586           0.000000      0.00000   \n",
              "Firewood             0.00000           1.076666      0.00000   \n",
              "Gas                  0.00000           0.000000      0.51902   \n",
              "Other                0.00000           0.000000      0.00000   \n",
              "\n",
              "             Pressure cooker (gas)  Oven (gas)  \\\n",
              "Charcoal                  0.000000    0.000000   \n",
              "Electricity               0.000000    0.000000   \n",
              "Firewood                  0.000000    0.000000   \n",
              "Gas                       0.263089    0.084545   \n",
              "Other                     0.000000    0.000000   \n",
              "\n",
              "             Sigiri (traditional, energy briquettes)  \\\n",
              "Charcoal                                     0.00000   \n",
              "Electricity                                  0.00000   \n",
              "Firewood                                     0.00000   \n",
              "Gas                                          0.00000   \n",
              "Other                                        0.16909   \n",
              "\n",
              "             Sigiri (high-efficiency, energy briquettes)  Stove (kerosene)  \n",
              "Charcoal                                        0.000000          0.000000  \n",
              "Electricity                                     0.000000          0.000000  \n",
              "Firewood                                        0.000000          0.000000  \n",
              "Gas                                             0.000000          0.000000  \n",
              "Other                                           0.012425          0.012425  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cc23dee0-8003-48aa-9eaa-492f5b54dffc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuel Source %</th>\n",
              "      <th>Sigiri (traditional, charcoal)</th>\n",
              "      <th>Sigiri (high-efficiency, charcoal)</th>\n",
              "      <th>Pressure cooker (charcoal)</th>\n",
              "      <th>Hot plate</th>\n",
              "      <th>Cooking coils</th>\n",
              "      <th>Pressure cooker (electric)</th>\n",
              "      <th>Percolator</th>\n",
              "      <th>Rice cooker</th>\n",
              "      <th>Stove (electric)</th>\n",
              "      <th>Deep fryer (electric)</th>\n",
              "      <th>Oven (electric)</th>\n",
              "      <th>Stones (firewood)</th>\n",
              "      <th>Stove (gas)</th>\n",
              "      <th>Pressure cooker (gas)</th>\n",
              "      <th>Oven (gas)</th>\n",
              "      <th>Sigiri (traditional, energy briquettes)</th>\n",
              "      <th>Sigiri (high-efficiency, energy briquettes)</th>\n",
              "      <th>Stove (kerosene)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Charcoal</th>\n",
              "      <td>68.901347</td>\n",
              "      <td>51.268175</td>\n",
              "      <td>17.161557</td>\n",
              "      <td>0.471615</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Electricity</th>\n",
              "      <td>28.961393</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.968791</td>\n",
              "      <td>7.184165</td>\n",
              "      <td>4.141085</td>\n",
              "      <td>3.848824</td>\n",
              "      <td>0.840858</td>\n",
              "      <td>0.685949</td>\n",
              "      <td>0.14586</td>\n",
              "      <td>0.14586</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Firewood</th>\n",
              "      <td>1.076666</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.076666</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gas</th>\n",
              "      <td>0.866654</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.51902</td>\n",
              "      <td>0.263089</td>\n",
              "      <td>0.084545</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>0.193940</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.16909</td>\n",
              "      <td>0.012425</td>\n",
              "      <td>0.012425</td>\n",
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   {\n      \"column\": \"Percolator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.721246329120191,\n        \"min\": 0.0,\n        \"max\": 3.848823797934723,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.848823797934723,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rice cooker\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37604321865051277,\n        \"min\": 0.0,\n        \"max\": 0.8408581993803633,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8408581993803633,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stove (electric)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30676574165209675,\n        \"min\": 0.0,\n        \"max\": 0.6859490515022271,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.6859490515022271,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deep fryer (electric)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06523075427924088,\n        \"min\": 0.0,\n        \"max\": 0.14586040079196794,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.14586040079196794,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Oven (electric)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06523075427924088,\n        \"min\": 0.0,\n        \"max\": 0.14586040079196794,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.14586040079196794,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stones (firewood)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4814996047352856,\n        \"min\": 0.0,\n        \"max\": 1.0766658473273782,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0766658473273782,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stove (gas)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2321127673102989,\n        \"min\": 0.0,\n        \"max\": 0.5190199261514193,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.5190199261514193,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pressure cooker (gas)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11765695308885303,\n        \"min\": 0.0,\n        \"max\": 0.26308894513217923,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.26308894513217923,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Oven (gas)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03780967794333778,\n        \"min\": 0.0,\n        \"max\": 0.08454501008867772,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.08454501008867772,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sigiri (traditional, energy briquettes)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07561935588667557,\n        \"min\": 0.0,\n        \"max\": 0.16909002017735544,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.16909002017735544,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sigiri (high-efficiency, energy briquettes)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005556693883046448,\n        \"min\": 0.0,\n        \"max\": 0.012425145252649124,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.012425145252649124,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stove (kerosene)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005556693883046448,\n        \"min\": 0.0,\n        \"max\": 0.012425145252649124,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.012425145252649124,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Prepare matrix for inner and outer percentages\n",
        "fuel_appliance_matrix = {}\n",
        "\n",
        "# Add inner pie percentages (fuel sources)\n",
        "for fuel, fuel_percentage in zip(fuel_source_order, fuel_source_percentages_ordered):\n",
        "    fuel_appliance_matrix[fuel] = {'Fuel Source %': fuel_percentage}\n",
        "\n",
        "# Add outer pie percentages (appliances within fuel sources)\n",
        "for fuel, appliance, appliance_percentage in appliance_data:\n",
        "    if fuel not in fuel_appliance_matrix:\n",
        "        fuel_appliance_matrix[fuel] = {}\n",
        "    fuel_appliance_matrix[fuel][appliance] = appliance_percentage\n",
        "\n",
        "# Convert to DataFrame\n",
        "matrix_df = pd.DataFrame(fuel_appliance_matrix).T.fillna(0)\n",
        "\n",
        "# Display the matrix\n",
        "print(\"Fuel Source and Appliance Percentages:\")\n",
        "display(matrix_df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHiAzk41NQ3b"
      },
      "source": [
        "# Cooking behaviors"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ME8k6KXpNJI8"
      },
      "source": [
        "## Meal skipping"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YAnp6AHLXajA"
      },
      "outputs": [],
      "source": [
        "# Define the column names\n",
        "columns = [\n",
        "    'Which of the following meals does the family skip at least once a week because there is not enough money to prepare?/Breakfast',\n",
        "    'Which of the following meals does the family skip at least once a week because there is not enough money to prepare?/Lunch',\n",
        "    'Which of the following meals does the family skip at least once a week because there is not enough money to prepare?/Dinner',\n",
        "    'Which of the following meals does the family skip at least once a week because there is not enough money to prepare?/None',\n",
        "    'Which of the following meals does the family skip at least once a week because there is not enough money to prepare?/Tea and/or snacks'\n",
        "]\n",
        "\n",
        "# Calculate the total number of respondents\n",
        "total_respondents = len(df_cooking_survey['Spotlight Kampala Cooking Su...'])\n",
        "\n",
        "# Temporary DataFrame for processing\n",
        "cooking_survey_temp = df_cooking_survey['Spotlight Kampala Cooking Su...']\n",
        "\n",
        "# Calculate the percentage for each column\n",
        "skipped_meals_percentage = cooking_survey_temp[columns].apply(lambda col: (col.sum() / total_respondents) * 100)\n",
        "\n",
        "# Rename the index for cleaner labels\n",
        "skipped_meals_percentage.index = ['Breakfast', 'Lunch', 'Dinner', 'None', 'Tea and/or snacks']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_0RI95AUYUwt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "cecc2b81-22c0-4aa0-c6fd-ff2f35dbccfb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total respondents: 120\n",
            "Number of unique respondents with valid data: 5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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sJqYZroBkIjw8XD4+PmoycqOyFKqc0OUASAJC//tT8z8qqd27d6tEiRIJXQ4AAHhEdAYICwuTt7e307fn0D3dj7py5QqPXgIAAAAA4BEOh+6wsDB9+OGHypAhgwICApQ9e3brsh07dqhu3bravXt3nBQJAAAAAEBi5FDovnLlisqWLatJkyYpKChI+fPnt5ldukiRItq6davmzZsXZ4UCAAAAAJDYOBS6hwwZokOHDun777/Xrl271KRJE5vlHh4eqlKlin777bc4KRIAAAAAgMTIodC9bNky1atXT02bNn1sn2zZsun06dMOFwYAAAAAQGLnUOg+d+6cChQo8MQ+bm5uunnzpkNFAQAAAACQFDgUutOlS/fU2coPHDigTJkyOVQUAAAAAABJgasjH6pcubKWLl2q06dPK0uWLHbL//33X61atUpt27Z97gKB+HD1zEGldPdK6DIAJAFXTockdAkAAOAF4lDo7t+/v5YuXaoKFSpoxIgRunTpkiQpJCREv//+u/r37y83Nzf16tUrTosFnGXdl+0TugQASYiHp6f8/f0TugwAAPACsJiHn/X1DJYtW6aWLVvqxo0bkiRjjCwWi4wxSpMmjRYsWKC6devGabFAXAsPD5ePj482btwoLy/OdAOIG/7+/goODk7oMgAAQAyiM0BYWJi8vb2dvj2HQ7f04Hnd3333nXbs2KErV67I29tbZcuWVdu2bfmFH4lCfP/BAQAAAEhYiSp0A4kdoRsAAABIXuI7Azg0e/mj7t+/r6tXr+r+/ftxsToAAAAAAJIEh0N3ZGSkxo0bp6JFi8rd3V3+/v5yd3dX0aJFNX78eAI4AAAAACDZc+jy8hs3bqhWrVravn27UqRIoaCgIGXIkEGhoaE6deqUoqKiVK5cOa1evVqpU6d2Rt1AnODycgAAACB5SRSXlw8aNEjbtm3TW2+9pSNHjujo0aPatm2bjh49qiNHjqhZs2b6/fffNWjQoLiuFwAAAACARMOhM91ZsmRRYGCg/vjjj8f2KV26tM6dO6fTp08/V4GAM3GmGwAAAEheEsWZ7suXL6tGjRpP7FOjRg1duXLFoaIAAAAAAEgKHArduXPn1oULF57Y5+LFi8qVK5dDRQEAAAAAkBQ4FLo//PBD/fDDD9q/f3+My//++299//336tat2/PUBgAAAABAoubqyIdy586t6tWrq1SpUmrdurUqVqxonb188+bNmj17tmrVqqVcuXJp06ZNNp+tXLlynBQOAAAAAMCLzqGJ1FKkSCGLxaLoj1osFuuymNoeFhkZ6UidgFMwkRoAAACQvMR3BnDoTPegQYMeG6oBAAAAAMADDp3pBpIKznQDAAAAyUuieGQYAAAAAAB4OocuL4927tw5ff/999qzZ4/CwsLk4+Oj4sWLq1mzZsqUKVNc1QgAAAAAQKLk8OXlX331lXr16qWIiAg9ugp3d3eNGTNGnTp1ipMiAWfh8nIAAAAgeUkUE6l9//336tKli/z9/dW/f39VqlTJ+siwTZs2acKECdblTZs2jeuaAQAAAABIFBw6012iRAmdPn1ae/fuVWBgoN3y06dPq3jx4goODtbu3bvjpFDAGTjTDQAAACQviWIitZCQEDVt2jTGwC1JWbJkUZMmTRQSEvJcxQEAAAAAkJg5FLp9fX2VOnXqJ/bx8vKSr6+vI6sHAAAAACBJcCh0v/baa1q+fLnu378f4/J79+5p+fLlev3115+rOAAAAAAAEjOHQvfo0aOVOnVq1axZU9u3b7dZtm3bNtWsWVNp0qTRZ599FidFAgAAAACQGDk0kVqOHDl09+5dnTt3TpLk6uoqf39/Xbp0yXr2O1OmTEqVKpXtxiwWHTlyJA7KBuIGE6kBAAAAyUuieGRYVFSUUqZMqeDgYJv2RydWezTPO/hIcAAAAAAAEiWHQvfx48fjuAwAAAAAAJIeh+7pBgAAAAAAT0foBgAAAADASWJ1efmwYcNksVj0wQcfKG3atBo2bFisVm6xWDRw4MDnKhAAAAAAgMQqVrOXp0iRQhaLRSEhIcqTJ49SpIjdCXKLxaLIyMjnLhJwluiZCzdu3CgvL6+ELgdAPPP397ebFBQAACRtL+Ts5evXr5ck6z9Mot8DSUWVKlUSugQACcDD01MHQkII3gAAwGliFbofDSQEFCQ1NTp/rfQ5SyZ0GQDi0ZXTIVo1toUuXbpE6AYAAE7j0CPDDh06pDx58jy136JFi9S4cWNHNgHEK7/MeZUhV4mELgMAAABAEuPQ7OU1a9bUuXPnnthn0aJFevvttx0qCgAAAACApMCh0H3t2jXVqlVLYWFhMS5fsmSJmjdvruzZsz9XcQAAAAAAJGYOhe6ff/5Zhw8fVv369RUREWGz7KefflKzZs2ULVs2JlwDAAAAACRrDoXuqlWrau7cufr999/15ptvKioqStKDMP7mm28qa9as2rBhgzJlyhSnxQIAAAAAkJg4FLolqVGjRvryyy+1bNkytW/f3hq4g4ODtX79egUGBsZlnQAAAAAAJDoOzV4erWPHjgoNDdXQoUM1c+ZM6yXlWbJkiav6AAAAAABItGJ1pvvkyZOPfbVt21avv/66/Pz8NGvWLBljbJYj6dqwYYMsFosWLVr01L47d+5U+fLllTp1alksFu3du9f5BQIAAABAAovVme5s2bLJYrE8sY8xRlWrVrVps1gsun//vsPF4f/MmjVLbdu2tWkLCAhQwYIF1bt3b9WpUyeBKnu6e/fuqUmTJnJ3d9e4cePk6emprFmzxuk25s+frwsXLqhbt25xul4AAAAAeB6xCt2tWrV6auhG/Bg2bJiyZ88uY4xCQ0M1a9Ys1a1bV8uXL1e9evUSurwYHTlyRCdOnND06dP13nvvOWUb8+fP1z///EPoBgAAAPBCiVXonjVrlpPLQGzVqVNHpUqVsr5/9913lSFDBi1YsOCxofv+/fuKiopSqlSp4qtMGxcuXJAk+fr6Jsj2AQAAACChODx7OV4Mvr6+8vDwkKvrg99Pjh8/LovFojFjxmj8+PHKmTOn3Nzc9O+//0qSDhw4oMaNGytt2rRyd3dXqVKltGzZMpt1XrlyRT179lThwoXl5eUlb29v1alTR3/99ddT64mIiFC9evXk4+Oj33//XW3atFGVKlUkSU2aNJHFYrHehrBv3z61adNGOXLkkLu7uzJmzKh33nlHly9ftlnn9evX1a1bN2XLlk1ubm5Knz69XnnlFf3555+SHjzCbsWKFTpx4oQsFossFouyZcv2PF8rAAAAAMQJh2Yvv379ui5evKigoCClTJnS2v7DDz9o2bJlcnd31wcffKASJUrEWaF4ICwsTJcuXZIxRhcuXNCkSZN048YNtWjRwqbfzJkzdefOHbVv315ubm5Kmzat9u/frwoVKihz5szq27evUqdOrR9//FENGjTQ4sWL9cYbb0iSjh49qp9//llNmjRR9uzZFRoaqmnTpqlKlSr6999/H/s4uNu3b+v111/Xrl27tG7dOpUuXVoWi0WZM2fWiBEj1LVrV5UuXVoZMmSQJK1du1ZHjx5V27ZtlTFjRu3fv19ff/219u/fr+3bt1tvaejYsaMWLVqkzp07q0CBArp8+bK2bNmikJAQlShRQv3791dYWJhOnz6tcePGSZK8vLycNQQAAAAAEGsOhe7evXtr7ty5Cg0NtYbuKVOmqHPnzjLGSJK+//577d69W/ny5Yu7aqEaNWrYvHdzc9O3336rV155xab99OnT+u+//xQQEGDz2eDgYO3cuVNubm6SpE6dOqlixYrq06ePNXQXLlxYhw4dUooU/3chRMuWLZUvXz7NmDFDAwcOtKvrxo0bqlevnvbv36/ffvtNxYoVkySVK1dOERERGjFihCpVqqTGjRtbP9OpUyf16NHDZj0vvfSS3nrrLW3ZskWVKlWSJK1YsULt2rXT2LFjrf169+5t/e9XXnlFmTNn1tWrV+1+fHhURESEIiIirO/Dw8Of2B8AAAAAnodDl5dv3LhRNWrUkKenp7Xts88+U+bMmbVp0yb9+OOPMsbo888/j7NC8cBXX32ltWvXau3atZo7d66qVaum9957T0uWLLHp16hRI5vAfeXKFf32229q2rSprl+/rkuXLunSpUu6fPmyatWqpcOHD+vMmTOSHgT56MAdGRmpy5cvy8vLS3nz5rVe0v2wsLAw1axZUwcOHNCGDRusgftpPDw8rP99584dXbp0SS+99JIk2WzH19dXO3bs0NmzZ2P3JT3ByJEj5ePjY30FBQU99zoBAAAA4HEcOtN97tw51a5d2/o+JCREp06d0ujRo1WxYkVJ0qJFi7Rp06a4qRJWZcqUsZlI7a233lLx4sXVuXNnm4nUsmfPbvO5//77T8YYDRw4MMYz1dKDCc8yZ86sqKgoTZgwQZMnT9axY8cUGRlp7ZMuXTq7z3Xr1k137tzRnj17VLBgwVjvy5UrVzR06FB9//331snWooWFhVn/e/To0WrdurWCgoJUsmRJ1a1bV61atVKOHDliva1o/fr1U/fu3a3vw8PDCd4AAAAAnMahM90RERE2M2Fv3LhRFotFNWvWtLblyJHDeuYUzpMiRQpVq1ZN586d0+HDh63tD59FlqSoqChJUs+ePa1nyh995cqVS5I0YsQIde/eXZUrV9bcuXO1evVqrV27VgULFrSu52Gvv/66jDH67LPPYlz+OE2bNtX06dPVsWNHLVmyRGvWrNGqVats6o3ud/ToUU2aNEmBgYH6/PPPVbBgQa1cuTL2X9T/5+bmJm9vb5sXAAAAADiLQ2e6s2TJon379lnf/+9//1PatGlVpEgRa1v0Jclwvvv370t6cF916tSpY+wTfVY4ZcqUdveFP2rRokWqVq2aZsyYYdN+7do1+fv72/Vv0KCBatasqTZt2ihNmjSaMmXKU2u+evWqfv31Vw0dOlSDBg2ytj/8w8HDMmXKpE6dOqlTp066cOGCSpQooU8//VR16tSRJJ4jDwAAAOCF5NCZ7jp16mjNmjXq2bOnBgwYoFWrVql+/fo2fQ4dOqTg4OA4KRKPd+/ePa1Zs0apUqVS/vz5H9svffr0qlq1qqZNm6Zz587ZLb948aL1v11cXKwT4kVbuHDhE69caNWqlSZOnKipU6eqT58+T63bxcVFkuy2M378eJv3kZGRNpeaR+9LYGCgzYRoqVOntusHAAAAAAnNoTPd/fr10/Lly/XFF19IenAWctiwYdblFy5c0NatW9W5c+e4qRJWK1eu1IEDByQ9+J7nz5+vw4cPq2/fvvL29taVK1ce+9mvvvpKFStWVOHChdWuXTvlyJFDoaGh2rZtm06fPm19Dne9evU0bNgwtW3bVuXLl9fff/+tefPmPfUe6s6dOys8PFz9+/eXj4+PPv7448f29fb2VuXKlTV69Gjdu3dPmTNn1po1a3Ts2DGbftevX1eWLFnUuHFjFS1aVF5eXlq3bp127txpM5t5yZIl9cMPP6h79+4qXbq0vLy87H4IAgAAAID45lDojn6m8q+//ipJqly5ss29sZcuXdLnn3+uWrVqxU2VsHr4Umx3d3fly5dPU6ZMUYcOHZ762QIFCmjXrl0aOnSoZs2apcuXLyt9+vQqXry4zXo//vhj3bx5U/Pnz9cPP/ygEiVKaMWKFerbt+9Tt/Hxxx8rLCzMGrw/+OCDx/adP3++unTpoq+++krGGNWsWVMrV660eQ64p6enOnXqpDVr1mjJkiWKiopSrly5NHnyZL3//vvWfp06ddLevXs1c+ZMjRs3TlmzZiV0AwAAAEhwFvPo9b1AMhIeHi4fHx81GblRWQpVTuhyAMSj0P/+1PyPSmr37t0qUaJEQpcDAADiSXQGCAsLi5eJlR26pxsAAAAAADwdoRsAAAAAACchdAMAAAAA4CSEbgAAAAAAnITQDQAAAACAkxC6AQAAAABwEoee0/044eHh2rFjh9zd3VWxYkVZLJa4XD0AAAAAAImKQ6F7+vTpmjt3rn7++Wf5+flJkv766y/VqVNHoaGhkqRy5cppzZo18vT0jLtqASe5euagUrp7JXQZAOLRldMhCV0CAABIBhwK3XPmzNHdu3etgVuSevTooQsXLqht27YKDQ3VL7/8oilTpqhHjx5xVizgLOu+bJ/QJQBIAB6envL390/oMgAAQBJmMcaYZ/1QxowZ9frrr2vatGmSpMuXLyt9+vRq166dpk6dKkl66aWXdPfuXf35559xWzEQh8LDw+Xj46ONGzfKy4sz3UBy4+/vr+Dg4IQuAwAAxKPoDBAWFiZvb2+nb8+hM93Xrl1TQECA9f3mzZslSQ0bNrS2VaxYUd9+++1zlgfEj2LFisXLHxwAAACA5MWh2cvTpUunc+fOWd//+uuvcnFxUYUKFaxtxhjdu3fv+SsEAAAAACCRcih0FylSREuXLtU///yj//77T/Pnz1eFChWUOnVqa5/jx48rU6ZMcVYoAAAAAACJjUOhu3fv3rp69aqKFi2qvHnz6tq1a+revbt1eVRUlLZs2aKSJUvGWaEAAAAAACQ2Dt3TXa1aNS1btkwzZ86UJDVr1kz169e3Lt+6dasCAwNt7vEGAAAAACC5cWj2ciCpiO+ZCwEAAAAkrPjOAA5dXg4AAAAAAJ4uVpeXb9q0yeENVK5c2eHPAgAAAACQmMUqdFetWlUWi8WhDURGRjr0OQAAAAAAErtYhe5BgwY5HLoBAAAAAEiumEgNyRoTqQEAAADJCxOpAQAAAACQRBC6AQAAAABwkljd0x2T69ev68svv9S6det09uxZRURE2PWxWCw6cuTIcxUIAAAAAEBi5VDovnjxosqXL68jR47I29vbek383bt3dfv2bUlSYGCgUqZMGafFAgAAAACQmDh0efmQIUN05MgRzZ49W1evXpUkffTRR7p586Z27NihMmXKKFu2bNq/f3+cFgsAAAAAQGLiUOj+5Zdf9PLLL6tFixZ2jxIrXbq0Vq5cqePHj2vo0KFxUiQAAAAAAImRQ6H73LlzKl68uPW9i4uL9bJySfLz81OdOnX0448/Pn+FAAAAAAAkUg6Fbh8fH927d8/63s/PT6dPn7bp4+3trdDQ0OerDgAAAACARMyh0J0jRw4dP37c+r548eJau3atLl++LEm6ffu2li9fruDg4DgpEgAAAACAxMih0F2zZk39+uuvunXrliSpQ4cOunDhgooWLaomTZqoUKFCOnLkiNq0aROXtQIAAAAAkKg4FLo7duyo6dOnW0N3w4YN9fnnn+vmzZtavHixzp8/r+7du6tXr15xWiwAAAAAAImJxRhj4mplkZGRunTpktKnT283qznwIop+xnxYWJi8vb0TuhwAAAAAThbfGcA1Llfm4uKiDBkyxOUqAQAAAABItJ4rdO/Zs0cLFizQgQMHdOvWLa1bt06SdOLECe3YsUM1atRQ2rRp46RQAAAAAAASG4dDd+/evTV27FhFX53+8OXkxhg1b95cY8eO1Ycffvj8VQIAAAAAkAg5NJHazJkzNWbMGNWrV0/79u1Tv379bJZny5ZNZcqU0bJly+KkSAAAAAAAEiOHznRPnjxZ+fPn1+LFi+Xq6qpUqVLZ9cmXL5/1cnMAAAAAAJIjh850//vvv3rllVfk6vr4zJ4hQwZduHDB4cIAAAAAAEjsHDrT7erqqrt37z6xz9mzZ+Xl5eVQUUB827t3L8crkAD8/f0VHByc0GUAAAA4jUOhu3Dhwvrtt98UGRkpFxcXu+XRM5mXLFnyuQsE4kOVKlUSugQgWfLw9NSBkBCCNwAASLIcCt3vvPOO3nvvPXXs2FFffvmlzbLw8HC99957On/+vCZMmBAnRQLOVqPz10qfkx+JgPh05XSIVo1toUuXLhG6AQBAkuVw6F63bp1mzJihH374Qb6+vpKkMmXKKCQkRDdv3lSbNm3UuHHjuKwVcBq/zHmVIVeJhC4DAAAAQBLj0ERqkjR//nxNmzZN2bNn15kzZ2SM0a5duxQcHKwpU6bo22+/jcs6AQAAAABIdBw60x2tXbt2ateunW7fvq2rV6/K29ubyagAAAAAAPj/nit0R/Pw8JCHh0dcrAoAAAAAgCTD4cvLAQAAAADAk8X6THeOHDmeeeUWi0VHjhx55s8BAAAAAJAUxDp0Hz9+XC4uLnJ1jZMr0oHnMmvWLLVt21Y7d+5UqVKlErocAAAAAIjRM19eXrVqVc2ePVvh4eG6ffv2U19I3GbNmiWLxaJdu3YldCkAAAAAkOjEOnT/+++/+vDDD7V37141a9ZMgYGB+uijj/T33387sz4AAAAAABKtWIfufPnyacyYMTp9+rQWL16scuXK6auvvlKxYsVUqlQpTZkyRWFhYc6sFQAAAACAROWZLy93cXFRgwYNtGzZMp06dUojRozQzZs39cEHHygwMFAtWrTQyZMnnVErXkBt2rRRtmzZ7NqHDBkii8Vi02axWNS5c2f9/PPPKlSokNzc3FSwYEGtWrXK7vNnzpzRu+++q8DAQLm5uSl79ux6//33dffuXZt+ERER6t69uwICApQ6dWq98cYbunjxYpzuIwAAAAA46rkeGZYhQwb16dNHISEhWrt2rdKmTasFCxZo7969cVQekpotW7aoU6dOatasmUaPHq07d+6oUaNGunz5srXP2bNnVaZMGX3//fd68803NXHiRLVs2VIbN27UrVu3bNbXpUsX/fXXXxo8eLDef/99LV++XJ07d47v3QIAAACAGD33VOQ7d+7Ut99+q++//15hYWHKnDmzsmTJEhe1IQkKCQnRv//+q5w5c0qSqlWrpqJFi2rBggXWsNyvXz+dP39eO3bssJmZfNiwYTLG2KwvXbp0WrNmjfWselRUlCZOnKiwsDD5+PjYbT8iIkIRERHW9+Hh4XG+jwAAAAAQzaEz3ZcuXdK4ceNUpEgRvfTSS/r222/18ssva8WKFTpx4oRKlCgR13UiiahRo4Y1cEtSkSJF5O3traNHj0p6EJp//vln1a9fP8ZHgT16yXr79u1t2ipVqqTIyEidOHEixu2PHDlSPj4+1ldQUFBc7BYAAAAAxCjWoTsqKkr/+9//1LBhQ2XOnFk9evSQJI0dO1ZnzpzRokWLVKdOHaVI8VxXrCOJCw4Otmvz8/PT1atXJUkXL15UeHi4ChUq5ND6/Pz8JMm6vkf169dPYWFh1tepU6eepXwAAAAAeCaxvrw8S5YsCg0NlY+Pj95991298847MZ6JRPLy6JnnaJGRkTG2u7i4xNj+6GXjsfWs63Nzc5Obm5tD2wIAAACAZxXr0H3+/HmlTJlSRYsW1fHjxzVo0KCnfsZisWjFihXPVSBebH5+frp27Zpd++Mu736agIAAeXt7659//nnOygAAAAAg4T3TRGr37t3Txo0bY93/cWdBkXTkzJlTYWFh2rdvn4oUKSJJOnfunH766SeH1pciRQo1aNBAc+fO1a5du+yupjDGcFwBAAAASDRiHbqPHTvmzDrwgvv2229jfJ52y5Yt1adPH73xxhvq2rWrbt26pSlTpihPnjz6888/HdrWiBEjtGbNGlWpUkXt27dX/vz5de7cOS1cuFBbtmyRr6/vc+4NAAAAAMSPWIfurFmzOrMOvOCmTJkSY3ubNm30008/qXv37urdu7eyZ8+ukSNH6vDhww6H7syZM2vHjh0aOHCg5s2bp/DwcGXOnFl16tSRp6fn8+wGAAAAAMQri3F0BisgCQgPD5ePj4+ajNyoLIUqJ3Q5QLIS+t+fmv9RSe3evZtHTQIAgHgTnQHCwsLk7e3t9O3xfC8AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAncU3oAoAXwdUzB5XS3SuhywCSlSunQxK6BAAAAKcjdAOS1n3ZPqFLAJIlD09P+fv7J3QZAAAATkPoBiRt3LhRXl6c6Qbim7+/v4KDgxO6DAAAAKchdAOSihUrJm9v74QuAwAAAEASw0RqAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAAAJzENaELAF4Ee/fulZeXV4Js29/fX8HBwQmybQAAAADORegGJFWpUiXBtu3h6akDISEEbwAAACAJInQDkmp0/lrpc5aM9+1eOR2iVWNb6NKlS4RuAAAAIAkidAOS/DLnVYZcJRK6DAAAAABJDBOpAQAAAADgJIRuAAAAAACchNANAAAAAICTELoBAAAAAHASQjcAAAAAAE5C6AYAAAAAwEkI3QksW7ZsatOmTUKX8UKyWCzq3LlzQpcBAAAAAA57oUK3xWKJ1WvDhg0JXWq8KFmypDp16pTQZQAAAAAAHOSa0AU8bM6cOTbvZ8+erbVr19q158+fPz7LShDnzp3Tnj17NGzYsIQuBQAAAADgoBcqdLdo0cLm/fbt27V27Vq79uRg5cqVcnd3V/Xq1Z97XTdv3lTq1KnjoCoAAAAAwLN4oS4vj42oqCiNHz9eBQsWlLu7uzJkyKAOHTro6tWrNv2WLl2qV199VYGBgXJzc1POnDk1fPhwRUZGPnUbJ06cUKdOnZQ3b155eHgoXbp0atKkiY4fP27Tb9asWbJYLNq6dau6d++ugIAApU6dWm+88YYuXrxo09cYo08++URZsmSRp6enqlWrpv379z+2hhUrVqhatWry8PCwti1cuFAlS5aUh4eH/P391aJFC505c8bmc23atJGXl5eOHDmiunXrKk2aNHr77bcfu53r16+rW7duypYtm9zc3JQ+fXq98sor+vPPP619qlatqkKFCunff/9VtWrV5OnpqcyZM2v06NE267p7964GDRqkkiVLysfHR6lTp1alSpW0fv16u+1GRUVpwoQJKly4sNzd3RUQEKDatWtr165dj61Vkj755BOlSJFCkyZNsrZNmjRJBQsWlKenp/z8/FSqVCnNnz//iesBAAAAgPiQ6EJ3hw4d1KtXL1WoUEETJkxQ27ZtNW/ePNWqVUv37t2z9ps1a5a8vLzUvXt3TZgwQSVLltSgQYPUt2/fp25j586d+v3339WsWTNNnDhRHTt21K+//qqqVavq1q1bdv27dOmiv/76S4MHD9b777+v5cuX200ANmjQIA0cOFBFixbV559/rhw5cqhmzZq6efOm3fru3bundevWqW7dujb707RpU7m4uGjkyJFq166dlixZoooVK+ratWs2n79//75q1aql9OnTa8yYMWrUqNFj97Vjx46aMmWKGjVqpMmTJ6tnz57y8PBQSEiITb+rV6+qdu3aKlq0qMaOHat8+fKpT58+WrlypbVPeHi4vvnmG1WtWlWjRo3SkCFDdPHiRdWqVUt79+61Wd+7776rbt26KSgoSKNGjVLfvn3l7u6u7du3P7bWAQMGaNCgQZo2bZq6dOkiSZo+fbq6du2qAgUKaPz48Ro6dKiKFSumHTt2PHY9AAAAABBfXqjLy59my5Yt+uabbzRv3jw1b97c2l6tWjXVrl1bCxcutLbPnz/f5ixxx44d1bFjR02ePFmffPKJ3NzcHrudV199VY0bN7Zpq1+/vsqVK6fFixerZcuWNsvSpUunNWvWyGKxSHpwFnfixIkKCwuTj4+PLl68qNGjR+vVV1/V8uXLrf369++vESNG2G1/8+bNCg8P16uvvirpQQjv06ePChUqpE2bNsnd3V2SVLFiRdWrV0/jxo3T0KFDrZ+PiIhQkyZNNHLkyKd+pytWrFC7du00duxYa1vv3r3t+p09e1azZ8+27vu7776rrFmzasaMGapTp44kyc/PT8ePH1eqVKmsn2vXrp3y5cunSZMmacaMGZKk9evXa9asWeratasmTJhg7dujRw8ZY2Kss2fPnho3bpxmzpyp1q1b29RfsGBBLVy48Kn7Kj34biIiIqzvw8PDY/U5AAAAAHBEojrTvXDhQvn4+OiVV17RpUuXrK+SJUvKy8vL5jLmhwP39evXdenSJVWqVEm3bt3SgQMHnridhz977949Xb58Wbly5ZKvr6/NZdfR2rdvbw3SklSpUiVFRkbqxIkTkqR169bp7t276tKli02/bt26xbj9X375RQUKFFC2bNkkSbt27dKFCxfUqVMna+CWHvw4kC9fPq1YscJuHe+///4T9zGar6+vduzYobNnzz6xn5eXl8299alSpVKZMmV09OhRa5uLi4s1cEdFRenKlSu6f/++SpUqZfO9LV68WBaLRYMHD7bbzsPfj/TgsvzOnTtrwoQJmjt3rk3gjq7/9OnT2rlzZ6z2d+TIkfLx8bG+goKCYvU5AAAAAHBEogrdhw8fVlhYmNKnT6+AgACb140bN3ThwgVr3/379+uNN96Qj4+PvL29FRAQYA2NYWFhT9zO7du3NWjQIAUFBcnNzU3+/v4KCAjQtWvXYvxscHCwzXs/Pz9Jst5nHh2+c+fObdMvICDA2vdhK1assJ7lfvjzefPmteubL18+6/Jorq6uypIlyxP3Mdro0aP1zz//KCgoSGXKlNGQIUNsgnS0LFmy2AViPz8/u3vpv/vuOxUpUkTu7u5Kly6dAgICtGLFCpvv7ciRIwoMDFTatGmfWt/s2bP11VdfadKkSXrrrbfslvfp00deXl4qU6aMcufOrQ8++EBbt2597Pr69eunsLAw6+vUqVNPrQEAAAAAHJWoLi+PiopS+vTpNW/evBiXBwQESJKuXbumKlWqyNvbW8OGDVPOnDnl7u6uP//8U3369FFUVNQTt9OlSxfNnDlT3bp1U7ly5eTj4yOLxaJmzZrF+FkXF5cY1/O4S6Wf5NixYzpw4ICmTJnyzJ+N5ubmphQpYvd7StOmTVWpUiX99NNPWrNmjT7//HONGjVKS5YssV42LsVuH+fOnas2bdqoQYMG6tWrl9KnT2+9B/3IkSMO7UuFChW0d+9effnll2ratKldUM+fP78OHjyo//3vf1q1apUWL16syZMna9CgQTaX3Edzc3N74q0FAAAAABCXElXozpkzp9atW6cKFSrYXAL+qA0bNujy5ctasmSJKleubG0/duxYrLazaNEitW7d2uY+5zt37thNWBZbWbNmlfTgTH2OHDms7RcvXrQ7U7xixQr5+PioYsWKdp8/ePCg3SPEDh48aF3uqEyZMqlTp07q1KmTLly4oBIlSujTTz+1Cd2xsWjRIuXIkUNLliyxOSv+6GXkOXPm1OrVq3XlypWnnu3OlSuXRo8erapVq6p27dr69ddflSZNGps+qVOn1ptvvqk333xTd+/eVcOGDfXpp5+qX79+NpfjAwAAAEB8S1SXlzdt2lSRkZEaPny43bL79+9bQ3H0WdmHz8LevXtXkydPjtV2XFxc7M5ST5o0KVaPG4tJjRo1lDJlSk2aNMlmvePHj7fr+8svv6hmzZpydf2/30NKlSql9OnTa+rUqTaTgK1cuVIhISE2l6I/i8jISLvL5dOnT6/AwECb7cRWTN/7jh07tG3bNpt+jRo1kjEmxjPRMV0dUKRIEf3yyy8KCQlR/fr1dfv2beuyy5cv2/RNlSqVChQoIGOMzWz2AAAAAJAQEtWZ7ipVqqhDhw4aOXKk9u7dq5o1ayplypQ6fPiwFi5cqAkTJqhx48YqX768/Pz81Lp1a3Xt2lUWi0Vz5syJ9eXe9erV05w5c+Tj46MCBQpo27ZtWrdundKlS+dQ3QEBAerZs6dGjhypevXqqW7dutqzZ49Wrlwpf39/a7/bt29r/fr1mjp1qs3nU6ZMqVGjRqlt27aqUqWK3nrrLYWGhmrChAnKli2bPvroI4fqun79urJkyaLGjRuraNGi8vLy0rp167Rz506bs/yxVa9ePS1ZskRvvPGGXn31VR07dkxTp05VgQIFdOPGDWu/atWqqWXLlpo4caIOHz6s2rVrKyoqSps3b1a1atXsHrcmSS+99JKWLl2qunXrqnHjxvr555+VMmVK1axZUxkzZlSFChWUIUMGhYSE6Msvv9Srr75qd0YcAAAAAOJbogrdkjR16lSVLFlS06ZN08cffyxXV1dly5ZNLVq0UIUKFSQ9eITX//73P/Xo0UMDBgyQn5+fWrRooZdfflm1atV66jYmTJggFxcXzZs3T3fu3FGFChW0bt26WH32cT755BO5u7tr6tSpWr9+vcqWLas1a9bYnKX+7bffFBEREeNl3W3atJGnp6c+++wz9enTR6lTp9Ybb7yhUaNGydfX16GaPD091alTJ61Zs0ZLlixRVFSUcuXKpcmTJ8d69vNHazx//rymTZum1atXq0CBApo7d64WLlyoDRs22PSdOXOmihQpohkzZqhXr17y8fFRqVKlVL58+ceuv3r16vrxxx/VqFEjtWzZUvPnz1eHDh00b948ffHFF7px44ayZMmirl27asCAAc9cPwAAAADENYtxZLYvOEWnTp20a9cu/fHHHwldSrIRHh4uHx8fNRm5UVkKVX76B+JY6H9/av5HJbV7926VKFEi3rcPAAAAJDfRGSAsLEze3t5O316iO9OdlBUrVkz169dP6DIAAAAAAHGE0P0Cad++fUKXAAAAAACIQ4lq9nIAAAAAABITQjcAAAAAAE5C6AYAAAAAwEkI3QAAAAAAOAmhGwAAAAAAJ2H2ckDS1TMHldLdK963e+V0SLxvEwAAAED8IXQDktZ9mXCPa/Pw9JS/v3+CbR8AAACA8xC6AUkbN26Ul1f8n+mWJH9/fwUHByfItgEAAAA4F6EbkFSsWDF5e3sndBkAAAAAkhgmUgMAAAAAwEkI3QAAAAAAOAmhGwAAAAAAJyF0AwAAAADgJIRuAAAAAACchNANAAAAAICTELoBAAAAAHASQjcAAAAAAE5C6AYAAAAAwEkI3QAAAAAAOAmhGwAAAAAAJyF0AwAAAADgJK4JXQCQkIwxkqTw8PAErgQAAABAfIj+t390FnA2QjeStcuXL0uSgoKCErgSAAAAAPHp8uXL8vHxcfp2CN1I1tKmTStJOnnyZLz8weHFEh4erqCgIJ06dUre3t4JXQ7iGeOfvDH+4BhI3hj/5C0sLEzBwcHWLOBshG4kaylSPJjWwMfHh//BTca8vb0Z/2SM8U/eGH9wDCRvjH/yFp0FnL6deNkKAAAAAADJEKEbAAAAAAAnIXQjWXNzc9PgwYPl5uaW0KUgATD+yRvjn7wx/uAYSN4Y/+QtvsffYuJrnnQAAAAAAJIZznQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAgJMQupEkRUREqE+fPgoMDJSHh4fKli2rtWvX2vSZNm2asmfPrrRp06ply5YKDw+3WR4VFaXixYtrxIgR8Vk6ntGNGzc0ePBg1a5dW2nTppXFYtGsWbNi7BsSEqLatWvLy8vLOu4XL1606XPt2jW9/fbb8vPzU44cOTRjxgy79ezatUuenp46duyYM3YJsbRz50517txZBQsWVOrUqRUcHKymTZvq0KFDdn0Z+6Rn//79atKkiXLkyCFPT0/5+/urcuXKWr58uV1fxj95+PTTT2WxWFSoUCG7Zb///rsqVqwoT09PZcyYUV27dtWNGzds+pw5c0avvvqqvL29VaBAgRiPpSVLlih9+vQKCwtz2n4gdjZs2CCLxRLja/v27TZ9Gf+k6c8//9Rrr72mtGnTytPTU4UKFdLEiRNt+rwwY2+AJKhZs2bG1dXV9OzZ00ybNs2UK1fOuLq6ms2bNxtjjNm8ebOxWCzmww8/NBMmTDAZM2Y07du3t1nH1KlTTfbs2c2dO3cSYhcQS8eOHTOSTHBwsKlataqRZGbOnGnX79SpU8bf39/kzJnTTJgwwXz66afGz8/PFC1a1ERERFj7vfvuuyYwMNBMmDDBdOnSxVgsFrN161br8qioKFOuXDnTr1+/+Ng9PEGjRo1MxowZTZcuXcz06dPN8OHDTYYMGUzq1KnN33//be3H2CdNK1asMLVq1TJDhgwxX3/9tRk/frypVKmSkWSmTZtm7cf4Jw+nTp0ynp6eJnXq1KZgwYI2y/bs2WPc3d1N8eLFzZQpU0z//v2Nm5ubqV27tk2/l19+2eTLl89MnjzZvP3228bNzc0cO3bMuvz27dsme/bsNscXEs769euNJNO1a1czZ84cm9fFixet/Rj/pGn16tUmVapUpmzZsuaLL74wX3/9tenTp4/p1auXtc+LNPaEbiQ5O3bsMJLM559/bm27ffu2yZkzpylXrpwxxpg+ffqYatWqWZfPnDnTZMyY0fr+6tWrxt/f3yxevDj+CodD7ty5Y86dO2eMMWbnzp2PDd3vv/++8fDwMCdOnLC2rV271u4f6BkyZDDfffed9X2VKlVM3759re/nzJljAgMDzfXr152wN3gWW7dutQlNxhhz6NAh4+bmZt5++21rG2OffNy/f98ULVrU5M2b19rG+CcPb775pqlevbqpUqWKXeiuU6eOyZQpkwkLC7O2TZ8+3Ugyq1evNsYYc+vWLWOxWMzGjRuNMQ9+ZMmePbuZOnWq9TPDhw83xYoVM5GRkfGwR3ia6NC9cOHCJ/Zj/JOesLAwkyFDBvPGG288cTxepLHn8nIkOYsWLZKLi4vat29vbXN3d9e7776rbdu26dSpU7p9+7b8/Pysy9OmTatbt25Z3w8ZMkSFCxdWw4YN47V2PDs3NzdlzJjxqf0WL16sevXqKTg42NpWo0YN5cmTRz/++KO17UnHxs2bN9W3b1+NHDlSXl5ecbgXcET58uWVKlUqm7bcuXOrYMGCCgkJsbYx9smHi4uLgoKCdO3aNWsb45/0bdq0SYsWLdL48ePtloWHh2vt2rVq0aKFvL29re2tWrWSl5eX9Ri4c+eOjDHWY8BiscjX19d6DJw5c0afffaZJkyYoBQp+Ofzi+b69eu6f/++XTvjnzTNnz9foaGh+vTTT5UiRQrdvHlTUVFRNn1etLHnqEGSs2fPHuXJk8fmD0ySypQpI0nau3evSpcurVWrVmnNmjU6fPiwxo4da13+77//aurUqTH+P28kTmfOnNGFCxdUqlQpu2VlypTRnj17rO9Lly6tL774QocPH9bq1au1atUq67ExYsQIZc6cWS1btoy32vFsjDEKDQ2Vv7+/JMY+Obh586YuXbqkI0eOaNy4cVq5cqVefvllSYx/chAZGakuXbrovffeU+HChe2W//3337p//77dMZAqVSoVK1bMegz4+fkpZ86cGjFihI4dO6Z58+Zp79691mOgd+/eqlOnjipXruz8ncIzadu2rby9veXu7q5q1app165d1mWMf9K0bt06eXt768yZM8qbN6+8vLzk7e2t999/X3fu3JH0Ao69w+fIgRdUwYIFTfXq1e3a9+/fbySZqVOnmvv375uGDRsaSUaSCQoKMvv27TPGGFOzZk3TsWPH+C4bceBxl5dHt8+ePdvuM7169TKSrPfu79u3z2TJksV6bDRq1MhERkaao0ePGg8PD7Nt27b42BU4aM6cOUaSmTFjhjGGsU8OOnToYB2zFClSmMaNG5srV64YYxj/5ODLL780Pj4+5sKFC8YYY3d5+cKFC40ks2nTJrvPNmnSxObWsl9//dX4+flZj4Fu3boZYx7cyuLh4WGOHz/u5L3Bs9i6datp1KiRmTFjhlm6dKkZOXKkSZcunXF3dzd//vmnMYbxT6qKFCliPD09jaenp+nSpYtZvHix6dKli5FkmjVrZox58caeM91Icm7fvi03Nze7dnd3d+tyFxcXLV68WIcPH9auXbt06NAhFS5cWMuWLdMff/yh4cOH68yZM6pfv74CAwNVv359nT17Nr53BXHk9u3bkvTU40KSChcurMOHD2vnzp06fPiwFi1apBQpUqhHjx5q1KiRXnrpJS1ZskRFixZV9uzZNWzYMBlj4m9n8FgHDhzQBx98oHLlyql169aSGPvkoFu3blq7dq2+++471alTR5GRkbp7964kxj+pu3z5sgYNGqSBAwcqICAgxj5POwail0tS9erVdfLkSW3fvl0nT57UuHHjFBUVpa5du6pHjx7KmjWrpkyZonz58ilv3ryaOnWqc3YMsVK+fHktWrRI77zzjl577TX17dtX27dvl8ViUb9+/SQx/knVjRs3dOvWLbVq1UoTJ05Uw4YNNXHiRHXo0EHff/+9Dh8+/MKNvavjuwu8mDw8PBQREWHXHn25iYeHh7UtV65c1v++e/euevToocGDB8vf31+VKlVSpkyZtHz5cn322Wdq3ry5NmzY4PT6Efeixzy2x4W7u7vN5Ui//fab1qxZo4MHD+rgwYNq1qyZpk2bpmzZsumtt95SUFCQ2rZt6+S9wJOcP39er776qnx8fKzzOkiMfXKQL18+5cuXT9KDe/Vq1qyp+vXra8eOHYx/EjdgwAClTZtWXbp0eWyfpx0DD4+/JHl5eals2bLW9zNnztT58+fVt29frVu3Tr169dLcuXNlsVjUvHlz5c2bV9WqVYujPcLzypUrl15//XUtWbJEkZGRjH8SFT1ub731lk178+bNNW3aNG3btk2enp6SXpyx50w3kpxMmTLp3Llzdu3RbYGBgTF+bty4cXJ1dVXnzp116tQpbdmyRaNHj1bJkiU1evRobdy4UadPn3Zq7XCOTJkySdJjj4u0adPG+Euo9OB+wQ8//FB9+/ZV5syZ9eOPP6p8+fJq27atqlWrpg4dOmjevHlOrR9PFhYWpjp16ujatWtatWqVzd84Y5/8NG7cWDt37tShQ4cY/yTs8OHD+vrrr9W1a1edPXtWx48f1/Hjx3Xnzh3du3dPx48f15UrV556DDzu3wTSg4mY+vfvr88++0ypU6fWggUL1LhxYzVo0ECvv/66GjduzDHwAgoKCtLdu3d18+ZNxj+Jih63DBky2LSnT59eknT16tUXbuwJ3UhyihUrpkOHDik8PNymfceOHdbljzp37pw++eQTa/COvpQ8+g8y+v+eOXPGiZXDWTJnzqyAgACbyVWi/fHHHzEeE9GmTJmi69evq2fPnpKks2fP2vwPdWBgIMdFArpz547q16+vQ4cO6X//+58KFChgs5yxT36iLxkMCwtj/JOwM2fOWC//zJ49u/W1Y8cOHTp0yHoLQKFCheTq6mp3DNy9e1d79+594jEwbNgwZc+eXW+//bYkjoHE4ujRo3J3d5eXlxfjn0SVLFlSkv2/y6P//R4QEPDCjT2hG0lO48aNFRkZqa+//traFhERoZkzZ6ps2bIKCgqy+0zfvn1VuXJl1a5dW9L//XJ24MABSbI+fig2j6bCi6lRo0b63//+p1OnTlnbfv31Vx06dEhNmjSJ8TNXrlzR4MGD9fnnn1vv/8yQIYP1uJAeHBscFwkjMjJSb775prZt26aFCxeqXLlyMfZj7JOmCxcu2LXdu3dPs2fPloeHh/UHGMY/aSpUqJB++uknu1fBggUVHBysn376Se+++658fHxUo0YNzZ07V9evX7d+fs6cObpx48Zjj4FDhw7pyy+/1IQJE2SxWCRxDLxoLl68aNf2119/admyZapZs6ZSpEjB+CdRTZs2lSTNmDHDpv2bb76Rq6urqlat+uKN/XNPxQa8gJo0aWJcXV1Nr169zLRp00z58uWNq6ur9cH3D9uxY4dxc3MzBw8etGkvVaqUKV68uPnyyy9NsWLFTNmyZeOrfDyjSZMmmeHDh5v333/fSDINGzY0w4cPN8OHDzfXrl0zxhhz8uRJky5dOpMzZ04zceJEM2LECOPn52cKFy5snb34UZ06dTJVqlSxadu3b5+xWCymY8eOZuTIkcbd3d1MnjzZ2buIGHz44YdGkqlfv76ZM2eO3SsaY580NWjQwFSvXt0MGTLETJ8+3QwfPtzky5fPSDJjx4619mP8k5dHZy83xpjdu3cbNzc3U7x4cTNlyhTTv39/4+7ubmrWrPnY9dStW9e0atXKpm358uUmRYoUpl+/fqZfv34mRYoU5pdffnHKfuDpqlWrZurWrWs++eQT8/XXX5tu3boZT09P4+PjY/79919rP8Y/aXrnnXeMJNO0aVPz1VdfmSZNmhhJpl+/ftY+L9LYE7qRJN2+fdv07NnTZMyY0bi5uZnSpUubVatW2fWLiooyZcuWNd27d7db9t9//5nKlSsbLy8vU7lyZXPkyJH4KB0OyJo1q/UxD4++jh07Zu33zz//mJo1axpPT0/j6+tr3n77bXP+/PkY17lv3z6TKlUqs2fPHrtls2bNMtmyZTPp0qUz3bt3N/fv33fSnuFJqlSp8thxf/Q3ZcY+6VmwYIGpUaOGyZAhg3F1dTV+fn6mRo0aZunSpXZ9Gf/kI6bQbYwxmzdvNuXLlzfu7u4mICDAfPDBByY8PDzGdaxYscJ4eXmZs2fP2i0bOXKkCQwMNJkyZTKjRo2K8/oRexMmTDBlypQxadOmNa6uriZTpkymRYsW5vDhw3Z9Gf+k5+7du2bIkCEma9asJmXKlCZXrlxm3Lhxdv1elLG3GMPzLgAAAAAAcAbu6QYAAAAAwEkI3QAAAAAAOAmhGwAAAAAAJyF0AwAAAADgJIRuAAAAAACchNANAAAAAICTELoBAAAAAHASQjcAAAAAAE5C6AYAAAAAwEkI3QAAIFk6f/68WrduraCgILm4uMhisejatWsJXVai16ZNG1ksFh0/fjyhSwGAFwKhGwCQLB0/flwWi8XmlSpVKgUFBal58+bat29fQpcYL5JzQGrTpo3mzJmjypUra8CAARo8eLDc3d0Tuiw4IPrvuU2bNgldCgDYcU3oAgAASEg5c+ZUixYtJEk3btzQ9u3btWDBAi1ZskS//vqrKlSokMAVwhnu3r2rtWvXqkaNGpo3b15ClwMASMII3QCAZC1XrlwaMmSITduAAQP06aefqn///tqwYUOC1AXnOn/+vKKiohQYGJjQpQAAkjguLwcA4BFdunSRJO3cudOmfenSpXr55Zfl5+cnd3d3FSpUSGPGjFFkZKRNv1mzZslisWjWrFlavny5KlSooDRp0ihbtmzWPnfv3tW4ceNUunRppUmTRl5eXipQoIC6d++uq1ev2qzvwoUL+uijj5QrVy65ubnJ399fjRo10j///GNXe7Zs2ZQtWzbduHFDH374oQIDA+Xm5qYiRYpo0aJFdn2/++47SVL27Nmtl9lXrVrV2uenn37SW2+9pVy5csnT01M+Pj6qVKmSFi9e/Njvb9q0aSpYsKDc3d0VFBSk3r17686dO3brjnb9+nUNHjxYBQsWlIeHh3x9fVWrVi1t2bLlsduIyc2bNzV48GDly5dP7u7uSps2rV599VVt3brVpl/VqlWVNWtWSdJ3331n3e+nXZq8YcMGWSwWDRkyRL///rtq1qwpX19fWSwWax9jjL799ltVqFBB3t7e8vT0VKlSpfTtt9/are/OnTsaO3asihYtKh8fH6VOnVrZsmVT06ZN9ddff1n7PXw8LV26VGXKlJGnp6cCAgL0zjvvKDQ0NMZ6t27dqldffVVp06aVu7u78uXLp8GDB+vWrVt2faPHJjQ0VK1bt5a/v788PDz00ksvPfaHp/3796tevXpKkyaNfHx8VLdu3RiPyYc58je0Zs0alS9fXp6enkqXLp1at26ty5cv2/TNnj27JNvxtFgs1tpj+10DgDNwphsAgMd4OEz169dPn332mTJnzqyGDRvKx8dHmzdvVq9evbRjxw4tXLjQ7vMLFy7UmjVrVK9ePXXq1Enh4eGSpNu3b+uVV17R1q1blTt3brVt21Zubm46fPiwpk2bplatWsnPz0+SdOTIEVWtWlWnT59WzZo11aBBA124cEGLFy/W6tWr9euvv6ps2bI22713755q1qypq1evqlGjRrp165a+//57NW3aVKtWrVLNmjUlSd26ddOsWbP0119/6cMPP5Svr68k2fw40K9fP6VKlUoVK1ZUpkyZdPHiRS1btkyNGzfWxIkTrT9QRBs0aJCGDx+uDBkyqF27dkqZMqV+/PFHHThwIMbv+MqVK6pcubL279+vChUqqGPHjgoPD9fSpUtVrVo1LVy4UA0aNHjqWN25c0fVq1fXH3/8oRIlSqhbt24KDQ3VDz/8oNWrV2vBggVq0qSJpAf3chcrVkwTJkxQ0aJFresvVqzYU7cjSb///rtGjBihatWqqX379jp58qSkB4H77bff1oIFC5Q7d241b95cqVKl0tq1a/Xuu+/q33//1ZgxY6zrad26tX788UcVKVLEegycOnVK69ev186dO1W0aFGb7UaPeePGjVWjRg1t375dM2fO1ObNm/XHH39YjxnpwbH31ltvyc3NTW+++abSp0+vNWvWaNiwYVq9erU2bNhgd//6tWvXVLFiRfn4+Khly5a6cOGCfvjhB9WqVUu7d+9WoUKFrH3/+ecfVahQQTdu3FDDhg2VO3du/fHHH6pQoYJd3dEc+RtatmyZVqxYofr166t8+fLatGmTZs+erSNHjlh/lClWrJg+/PBDu/GU/u9YftbvGgDilAEAIBk6duyYkWRq1aplt2zQoEFGkqlWrZoxxpg1a9ZY+964ccPaLyoqynTs2NFIMosWLbK2z5w500gyKVKkMGvXrrVbf48ePYwk07JlS3P//n2bZdeuXTPXr1+3vi9fvrxxcXExq1atsul38OBBkyZNGlO4cGGb9qxZsxpJ5vXXXzcRERHW9nXr1sW4v61btzaSzLFjx2L8no4cOWLXdv36dVO4cGHj4+Njbt68aVOTi4uLyZw5swkNDbW2h4eHmwIFChhJpkqVKjbrat68uZFkpk+fbtMeGhpqgoKCTEBAgLl9+3aMtT1s6NChRpJ5++23TVRUlLX9zz//NKlSpTK+vr4mPDzc2h49/q1bt37quqOtX7/eSDKSzLfffmu3/OuvvzaSTNu2bc3du3et7REREaZ+/fpGktm1a5cx5sE4WywWU7JkSbtj4P79++bq1avW99HHkyS746Bv375GkuncubO1LSwszPj4+Bg3Nzfz119/WdsjIyPNm2++aSSZYcOG2awnev2dOnUykZGR1vZvvvnGSDIdOnSw6V+lShUjycydO9emvV+/ftZ1PXxMOfo35OrqarZs2WLz3VStWtVIMtu2bbO2P2k8n+W7BgBnIHQDAJKl6H+k58yZ0wwePNgMHjzY9OzZ01SqVMlIMu7u7ub33383xhjz2muvGUnmxIkTduuJ/gd9o0aNrG3RgeGNN96w63/v3j2TJk0a4+PjY65cufLEGv/8808jybzzzjsxLu/evbuRZP7++29rW3ToPnr0qF3/rFmzmrRp09q0PS10P87YsWONJLNhwwZr25AhQ4wk88UXX9j1nz9/vl3ovnjxonFxcTHVq1ePcRsTJ040kszy5cufWk+OHDlMypQpzalTp+yWtWvXzkgys2fPtrY9T+guUaJEjMuLFCliUqdObW7dumW3bN++fUaS6dGjhzHmQTCWZCpUqGDzI0FMoo+nGjVq2C27fv268fX1Nd7e3tawPHv2bCPJvP/++3b9T5w4YVxdXU2OHDls2iWZ1KlT2/zgY8yD49XV1dVmn0+cOGEkmSJFijy2nkePKUf/hlq1avXY72PixInWtieN57N81wDgDFxeDgBI1o4cOaKhQ4dKklKmTKkMGTKoefPm6tu3rwoXLixJ2r59u1KnTh3jfbmS5OHhEePl02XKlLFrO3DggK5fv64aNWrYXA4ck+3bt0uSQkND7SZ7i15X9P99+NJfX19f6z2uD8uSJYu2bdv2xG0+6sKFC/rss8+0cuVKnThxQrdv37ZZfvbsWet/R98bW7FiRbv1xDQL/M6dOxUZGamIiIgY9+/w4cOSHuxfvXr1HltjeHi4jh49qvz58ytLlix2y6tVq6bp06dr7969atmy5WPXE1ulS5e2a7t165b+/vtvBQYGatSoUXbL7927J+n/xszb21t169bVL7/8ohIlSqhJkyaqWrWqSpcurZQpU8a43UqVKtm1eXl5qVixYtqwYYOOHj2qXLlyac+ePZIU4/3zwcHBypEjhw4dOqTr168rTZo01mV58uSRl5eXTX9XV1dlyJDB5vnlTxrnh+t5mKN/QyVLlrRrix7j2D5T3ZHvGgDiEqEbAJCs1apVS6tWrXpinytXruj+/fvWcB6Tmzdv2rVlyJDBri0sLEySlDlz5qfWduXKFUnSihUrtGLFilhv28fHJ8Z+rq6uioqKeup2H95+6dKldfLkSVWoUEE1atSQr6+vXFxctHfvXi1dulQRERHW/tH3rKdPn95uXTF9F9H7t3XrVrvJzh4W03f7sOjtxrQNScqUKZNNv+cV03auXr0qY4zOnDkT6+Nk4cKFGjFihObPn6/+/ftLehAQ27ZtqxEjRsjT0/Op2324PfrYis33cejQIYWHh9uEbm9v7xj7u7q62kx0Fr2dmMb5cdt19G8opppcXR/88/XRydee5Fm/awCIS8xeDgDAU3h7eytdunQyD27LivF17Ngxu889PBFbtOjJys6cOROr7UrSpEmTnrjt1q1bP98OPsaMGTN08uRJDR8+XFu2bNGkSZM0fPhwDRkyRC+99NJj671w4YLdsphm2I7u36NHjyfu3+DBg59YZ/R6HjeL9/nz5236Pa+YxjV63SVLlnzivqxfv976GU9PT33yySc6evSojh49qhkzZihv3ryaMGGCPvroI7ttPG7/otujf2xx9vcRvZ2Yxvlx23X0byiuPOt3DQBxidANAMBTlC1bVpcvX7Ze7vw88ubNK29vb+3cudPu0WAxbVfSM18S/ixcXFwkxXzW8MiRI5Kk119/3W7Z5s2b7dqiZ4CO6az177//btdWunRpWSyW594/b29v5ciRQ//991+MP2ZEX+oc29nJHZEmTRrlz59fISEhsb7s+WHZs2fXO++8o40bN8rLy0vLli2z6xPTd37jxg3t3bvX+h1IUvHixSUpxkd9nTp1SkeOHFGOHDlsznI/i+hxjumRbtH1PCou/4Zi8qTj+FGx+a4BIC4RugEAeIquXbtKkt555x2b5wNHO3/+vEJCQmK1LldXV3Xo0EFhYWH68MMP7UJCWFiYbty4IenBPeFly5bVggUL9MMPP9itKyoqShs3bnzW3bGRNm1aSQ/C2KOin2X9aLiaP3++fvnlF7v+zZo1U4oUKTR27FhdunTJ2n7z5k19+umndv0zZsyopk2b6vfff9fnn38uY4xdnx07dsT4XOlHtW7dWvfu3VO/fv1s1rNv3z7NmjVLPj4+sXr02PPo2rWrbt26pXbt2sV4qfSxY8d0/PhxSdLFixdjfKb11atXFRERYfc4L0lat26dVq9ebdP26aef6tq1a2rVqpVSpHjwz7rXX39dPj4+mjlzpvbv32/ta4xRnz59dP/+/ac+k/xJgoODVblyZe3bt0/z5s2zWTZixIgYf3SIy7+hmPj5+cliscR4HDvyXQNAXOKebgAAnqJ27doaOHCghg8frly5cql27drKmjWrLl++rP/++0+bN2/WJ598ovz588dqfcOGDdP27ds1Z84cbd++XXXq1JGbm5uOHj2qVatWacuWLdazsgsWLFC1atXUrFkzjR8/XiVKlJCHh4dOnjypbdu26eLFi7pz547D+1a9enWNGTNG7du3V6NGjZQ6dWplzZpVLVu2VMuWLTVq1Ch16dJF69evV9asWfXXX3/p119/VcOGDbVkyRKbdeXNm1d9+/bViBEjVLhwYTVt2lSurq5asmSJChcurH/++ccaDKNNnjxZBw8eVO/evTVnzhyVK1dOvr6+OnXqlHbt2qXDhw/r3LlzT73ntnfv3lqxYoXmzJmjkJAQvfzyy9bnTN+/f1/Tp093+MxubHXo0EHbt2/Xd999p61bt6pGjRoKDAxUaGioDhw4oB07dmj+/PnKli2bzpw5o+LFi6to0aIqUqSIMmfOrMuXL2vp0qW6d++eevbsabf+evXqqX79+mrcuLGyZcum7du3a/369cqZM6eGDRtm7eft7a3p06frrbfeUtmyZfXmm28qICBA69at0+7du1WmTBn16tXrufb1q6++UoUKFdSqVSv9/PPP1ud079y5U5UqVbI7Kx/Xf0OP8vLyUunSpbVp0ya1bNlSuXPnVooUKdSyZUtdvXr1mb9rAIhTTpwZHQCAF9aTntP9OGvXrjX169c3AQEBJmXKlCZjxoymXLlyZvjw4ebkyZPWftGPNJo5c+Zj13Xnzh0zZswYU6xYMePh4WG8vLxMgQIFTI8ePeyeG3zlyhUzYMAAU6hQIWvf3Llzm+bNm5slS5bY9M2aNavJmjVrjNuMfrbyo0aPHm1y585tUqZMafdYr71795qaNWsaPz8/kyZNGlOlShWzbt26J+7j5MmTTf78+U2qVKlMlixZTM+ePc2pU6eszw9/1K1bt8zo0aNNyZIlTerUqY2Hh4fJnj27adCggZk9e7a5d+/eY7/Hh924ccMMHDjQ5MmTx/ps7jp16pjNmzfb9X2eR4YNHjz4if1++OEHU6NGDePn52dSpkxpMmfObKpWrWrGjh1rLl68aIwx5urVq2bIkCGmcuXKJlOmTCZVqlQmMDDQ1K5d26xcudJmfQ9/1z///LMpXbq08fDwMOnSpTNt2rQx586di7GOTZs2mTp16hhfX1+TKlUqkydPHjNw4ECb52RHe3TcH/a4Y+rvv/82devWNV5eXiZNmjSmTp065u+//37iY+ji4m/oceNw8OBBU7duXePr62ssFouRZNavX/9M3zUAOIPFmBiu5QIAAIhD69at0yuvvKLevXvH+EgtPN6sWbPUtm1bzZw587kuCwcAJAzu6QYAAHHm4sWLdvepX7t2Tf369ZMkp99XDQDAi4Z7ugEAQJyZN2+exowZo+rVqyswMFDnzp3TqlWrdOHCBbVp00blypVL6BIBAIhXhG4AABBnypcvr5IlS2rdunW6cuWKXFxclD9/fg0cOFCdOnVK6PIAAIh33NMNAAAAAICTcE83AAAAAABOQugGAAAAAMBJCN0AAAAAADgJoRsAAAAAACchdAMAAAAA4CSEbgAAAAAAnITQDQAAAACAkxC6AQAAAABwEkI3AAAAAABO8v8APHkOhyo5qxEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a horizontal bar plot\n",
        "plt.figure(figsize=(10, 4))\n",
        "ax = skipped_meals_percentage.sort_values(ascending=True).plot(kind='barh', color='#71b3ff', edgecolor='black')\n",
        "\n",
        "# Add labels and title with smaller font sizes\n",
        "plt.xlabel('Percentage of respondents', fontsize=14)\n",
        "plt.ylabel('Meals skipped', fontsize=14)\n",
        "\n",
        "# Format x-axis tick labels as percentages\n",
        "ticks = ax.get_xticks()\n",
        "plt.xticks(ticks=ticks, labels=[f\"{int(tick)}%\" for tick in ticks], fontsize=12)\n",
        "\n",
        "# Retain y-axis text labels\n",
        "plt.yticks(fontsize=12)\n",
        "\n",
        "# Display the plot\n",
        "plt.tight_layout()\n",
        "\n",
        "print(f\"Total respondents: {total_respondents}\")\n",
        "\n",
        "plt.savefig(fig_path + \"Meal Skipping.png\", dpi=500, bbox_inches='tight')\n",
        "print(f\"Number of unique respondents with valid data: {skipped_meals_percentage.dropna().index.nunique()}\")\n",
        "\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aXa9XcRSNJ3M"
      },
      "source": [
        "## Fuel source purchase frequency"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VKbWvptkNKNJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "6cfdfdfe-13df-4737-909e-d4f864f20fd6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of respondents included in the graph: 104\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define connection categories (original order)\n",
        "connection_categories = ['Individual metered', 'Collective metered', 'Collective unmetered']\n",
        "charcoal_categories = ['Daily', 'Weekly', 'Monthly']\n",
        "\n",
        "# Create a crosstab to count occurrences\n",
        "charcoal_counts = pd.crosstab(\n",
        "    filtered_df['Connection type'],\n",
        "    filtered_df['Do you buy charcoal on a daily, weekly, or monthly basis?'],\n",
        "    dropna=False\n",
        ")\n",
        "\n",
        "# Reindex to ensure all categories are present\n",
        "charcoal_counts = charcoal_counts.reindex(index=connection_categories, columns=charcoal_categories, fill_value=0)\n",
        "\n",
        "# Print total number of respondents\n",
        "print(f\"Total number of respondents included in the graph: {charcoal_counts.sum().sum()}\")\n",
        "\n",
        "# Calculate proportions for 100% stacked bar chart\n",
        "charcoal_proportions = charcoal_counts.div(charcoal_counts.sum(axis=1), axis=0)\n",
        "\n",
        "# Reverse the y-axis order properly\n",
        "charcoal_proportions = charcoal_proportions.iloc[::-1]  # <-- This actually flips the order in the plot\n",
        "\n",
        "# Plot the horizontal 100% stacked bar chart\n",
        "fig, ax = plt.subplots(figsize=(10, 4))  # Wider for readability\n",
        "\n",
        "# Plot the stacked bars with black edges\n",
        "charcoal_proportions.plot(\n",
        "    kind='barh',\n",
        "    stacked=True,\n",
        "    color=['#71B3FE', '#D7CFD6', '#372E2A'],\n",
        "    edgecolor='black',  # Thin black borders\n",
        "    linewidth=0.8,\n",
        "    ax=ax\n",
        ")\n",
        "\n",
        "# Set axis labels with consistent font size\n",
        "ax.set_xlabel('Percent of respondents', fontsize=16, labelpad=10)\n",
        "ax.set_ylabel('Connection type', fontsize=16, labelpad=10)\n",
        "\n",
        "# Set tick labels with consistent font size\n",
        "ax.set_xticks(np.arange(0, 1.1, 0.2))  # Tick labels at every 20%\n",
        "ax.set_xticklabels([f'{int(x*100)}%' for x in np.arange(0, 1.1, 0.2)], fontsize=14)\n",
        "ax.set_yticklabels(connection_categories[::-1], fontsize=14)  # Properly reversed labels\n",
        "\n",
        "# Add a borderless legend to the right of the graphic\n",
        "legend = ax.legend(title='Frequency of\\nbuying charcoal', loc='center left', bbox_to_anchor=(1, .72), frameon=False, fontsize=14, title_fontsize=14)\n",
        "\n",
        "# Enhance aesthetics by removing unnecessary spines\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "ax.spines['left'].set_linewidth(0.8)\n",
        "ax.spines['bottom'].set_linewidth(0.8)\n",
        "\n",
        "ax.set_xlim(0, 1.0)\n",
        "\n",
        "# Add thin black border around the plot area\n",
        "for spine in ax.spines.values():\n",
        "    spine.set_visible(True)\n",
        "    spine.set_linewidth(0.8)\n",
        "    spine.set_edgecolor('black')\n",
        "\n",
        "# Adjust layout to prevent overlap\n",
        "plt.tight_layout()\n",
        "plt.savefig(fig_path + \"Charcoal Purchase Frequency.png\", dpi=500, bbox_inches='tight')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define columns\n",
        "charcoal_expense_col = 'How much do you spend on charcoal each ${charcoal_expense_unit}?'\n",
        "group_col_1 = 'Connection type'\n",
        "group_col_2 = 'Do you buy charcoal on a daily, weekly, or monthly basis?'\n",
        "\n",
        "# Step 1: Clean both group columns (creates a new DataFrame for safety)\n",
        "filtered_df_cleaned = filtered_df.copy()\n",
        "filtered_df_cleaned[group_col_1] = (\n",
        "    filtered_df_cleaned[group_col_1]\n",
        "    .astype(str)\n",
        "    .str.strip()\n",
        "    .str.lower()\n",
        ")\n",
        "\n",
        "filtered_df_cleaned[group_col_2] = (\n",
        "    filtered_df_cleaned[group_col_2]\n",
        "    .astype(str)\n",
        "    .str.strip()\n",
        "    .str.title()  # For display consistency: Daily, Weekly, Monthly\n",
        ")\n",
        "\n",
        "# Step 2: Print unique values to check consistency\n",
        "print(\"Unique connection types:\", filtered_df_cleaned[group_col_1].unique())\n",
        "print(\"Unique purchase frequencies:\", filtered_df_cleaned[group_col_2].unique())\n",
        "\n",
        "# Step 3: Define valid categories\n",
        "connection_categories = ['individual metered', 'collective metered', 'collective unmetered']\n",
        "charcoal_categories = ['Daily', 'Weekly', 'Monthly']\n",
        "\n",
        "# Step 4: Filter valid rows\n",
        "filtered_df_valid = filtered_df_cleaned[\n",
        "    filtered_df_cleaned[group_col_1].isin(connection_categories) &\n",
        "    filtered_df_cleaned[group_col_2].isin(charcoal_categories) &\n",
        "    pd.to_numeric(filtered_df_cleaned[charcoal_expense_col], errors='coerce').notna()\n",
        "].copy()\n",
        "\n",
        "# Step 5: Convert charcoal expense to numeric\n",
        "filtered_df_valid[charcoal_expense_col] = pd.to_numeric(filtered_df_valid[charcoal_expense_col])\n",
        "\n",
        "# Step 6: Group by both connection type and payment frequency\n",
        "grouped = filtered_df_valid.groupby([group_col_1, group_col_2])[charcoal_expense_col]\n",
        "summary_stats = grouped.agg(['count', 'mean', 'std']).reset_index()\n",
        "summary_stats['ci95'] = 1.96 * summary_stats['std'] / np.sqrt(summary_stats['count'])\n",
        "\n",
        "# Step 7: Sort and format labels\n",
        "summary_stats = summary_stats.sort_values(by=[group_col_1, group_col_2])\n",
        "summary_stats[group_col_1] = summary_stats[group_col_1].str.title()\n",
        "\n",
        "# Display the summary table\n",
        "print(\"\\nSummary statistics by connection type and charcoal purchase frequency:\")\n",
        "print(summary_stats)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhTTvSXv5wBY",
        "outputId": "6d8dbbb0-6d31-4957-e265-a391aae69365"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique connection types: ['collective metered' 'individual metered' 'collective unmetered']\n",
            "Unique purchase frequencies: ['Monthly' 'Daily' 'Weekly' 'Nan']\n",
            "\n",
            "Summary statistics by connection type and charcoal purchase frequency:\n",
            "        Connection type  \\\n",
            "0    Collective Metered   \n",
            "1    Collective Metered   \n",
            "2    Collective Metered   \n",
            "3  Collective Unmetered   \n",
            "4  Collective Unmetered   \n",
            "5  Collective Unmetered   \n",
            "6    Individual Metered   \n",
            "7    Individual Metered   \n",
            "8    Individual Metered   \n",
            "\n",
            "  Do you buy charcoal on a daily, weekly, or monthly basis?  count       mean  \\\n",
            "0                                              Daily            37   2.378378   \n",
            "1                                            Monthly            14  58.928571   \n",
            "2                                             Weekly            10   6.700000   \n",
            "3                                              Daily             8   1.875000   \n",
            "4                                            Monthly             2  55.000000   \n",
            "5                                             Weekly             1   5.000000   \n",
            "6                                              Daily            11   2.909091   \n",
            "7                                            Monthly            17  63.529412   \n",
            "8                                             Weekly             4   3.000000   \n",
            "\n",
            "         std      ci95  \n",
            "0   0.981817  0.316363  \n",
            "1   9.025300  4.727741  \n",
            "2   7.103207  4.402614  \n",
            "3   0.991031  0.686750  \n",
            "4   0.000000  0.000000  \n",
            "5        NaN       NaN  \n",
            "6   1.972539  1.165696  \n",
            "7  15.285277  7.266160  \n",
            "8   1.414214  1.385929  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare data\n",
        "summary_stats_collapsed = summary_stats.copy().rename(columns={\n",
        "    'Do you buy charcoal on a daily, weekly, or monthly basis?': 'purchase_frequency',\n",
        "    'Connection type': 'connection_type'\n",
        "})\n",
        "\n",
        "# Drop \"Weekly\"\n",
        "frequency_order = ['Daily', 'Monthly']\n",
        "colors = {'Daily': '#71B3FE', 'Monthly': '#372E2A'}\n",
        "filtered = summary_stats_collapsed[summary_stats_collapsed['purchase_frequency'].isin(frequency_order)]\n",
        "\n",
        "print(f\"n = {filtered['count'].sum()} original responses\")\n",
        "\n",
        "# Weighted mean expense\n",
        "collapsed_means = (\n",
        "    filtered\n",
        "    .groupby('purchase_frequency', group_keys=False)\n",
        "    .apply(lambda g: np.average(g['mean'], weights=g['count']))\n",
        "    .reindex(frequency_order)\n",
        ")\n",
        "\n",
        "# Mean confidence interval\n",
        "collapsed_errors = (\n",
        "    filtered\n",
        "    .groupby('purchase_frequency')['ci95']\n",
        "    .mean()\n",
        "    .reindex(frequency_order)\n",
        ")\n",
        "\n",
        "bar_colors = [colors[freq] for freq in frequency_order]\n",
        "\n",
        "# Conversion factor (UGX → kg charcoal)\n",
        "exchange_rate_ecooking = 3800\n",
        "conversion_factor = 1000 / 0.286 / exchange_rate_ecooking\n",
        "\n",
        "# Plot\n",
        "fig, ax = plt.subplots(figsize=(8.5, 3.5))\n",
        "y_pos = list(range(len(frequency_order)))\n",
        "\n",
        "# Main bars\n",
        "ax.barh(\n",
        "    y=y_pos,\n",
        "    width=collapsed_means.values,\n",
        "    xerr=collapsed_errors.values,\n",
        "    color='#71B3FE',\n",
        "    edgecolor='black',\n",
        "    capsize=5,\n",
        "    zorder=3  # ensure grid is behind\n",
        ")\n",
        "\n",
        "# Data labels to right of whiskers\n",
        "for i, (mean, error) in enumerate(zip(collapsed_means.values, collapsed_errors.values)):\n",
        "    label_x = mean + error + 0.5\n",
        "    ax.text(label_x, i, f\"{mean:.1f}\", va='center', ha='left', fontsize=14)\n",
        "\n",
        "# Y-axis setup\n",
        "ax.set_yticks(y_pos)\n",
        "ax.set_yticklabels(frequency_order, fontsize=16)\n",
        "ax.set_ylabel('Purchase frequency', fontsize=16)\n",
        "\n",
        "# X-axis setup\n",
        "ax.set_xlabel('Charcoal purchase expense (1,000 UGX)', fontsize=16)\n",
        "x_max = int(np.ceil((collapsed_means + collapsed_errors).max()) + 5)\n",
        "ax.set_xlim(0, x_max)\n",
        "ax.set_xticks(np.arange(0, x_max + 1, 10))\n",
        "ax.tick_params(axis='x', labelsize=14)\n",
        "\n",
        "# Gridlines behind bars (10-unit interval, solid black)\n",
        "ax.grid(which='major', axis='x', linestyle='-', color='black', linewidth=0.5, zorder=0)\n",
        "\n",
        "# Secondary x-axis: kg/mo\n",
        "def primary_to_secondary(x): return x / conversion_factor\n",
        "def secondary_to_primary(x): return x * conversion_factor\n",
        "\n",
        "secax = ax.secondary_xaxis('top', functions=(primary_to_secondary, secondary_to_primary))\n",
        "secax.set_xlabel('Estimated charcoal purchase quantity (kg)', fontsize=16)\n",
        "secax.tick_params(labelsize=14)\n",
        "\n",
        "# Border\n",
        "for spine in ax.spines.values():\n",
        "    spine.set_visible(True)\n",
        "    spine.set_linewidth(0.8)\n",
        "    spine.set_edgecolor('black')\n",
        "\n",
        "ax.set_xlim(0, 70.001)\n",
        "\n",
        "# Layout\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig(fig_path + \"Charcoal Purchase Quantity.png\", dpi=500, bbox_inches='tight')\n",
        "\n",
        "# Create a summary DataFrame with both x-axis values\n",
        "underlying_data = pd.DataFrame({\n",
        "    'Purchase frequency': frequency_order,\n",
        "    'Mean expense (1,000 UGX)': collapsed_means.values.round(2),\n",
        "    'Estimated quantity (kg)': (collapsed_means.values / conversion_factor).round(2)\n",
        "})\n",
        "\n",
        "print(\"Underlying data for both x-axes:\")\n",
        "display(underlying_data)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "id": "R9slqNj_xSFg",
        "outputId": "b9594cda-3265-49ed-e4bd-c89e40c4c57c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n = 89 original responses\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-505-5c1dcd216abf>:18: DeprecationWarning:\n",
            "\n",
            "DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Underlying data for both x-axes:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  Purchase frequency  Mean expense (1,000 UGX)  Estimated quantity (kg)\n",
              "0              Daily                      2.41                     2.62\n",
              "1            Monthly                     61.06                    66.36"
            ],
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              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('underlying_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        document.querySelector('#id_61921265-2fcb-4d0c-821f-3f2e1688a8d1 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('underlying_data');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "underlying_data",
              "summary": "{\n  \"name\": \"underlying_data\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Purchase frequency\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Monthly\",\n          \"Daily\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean expense (1,000 UGX)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 41.47181271659101,\n        \"min\": 2.41,\n        \"max\": 61.06,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          61.06,\n          2.41\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated quantity (kg)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45.070986232830535,\n        \"min\": 2.62,\n        \"max\": 66.36,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          66.36,\n          2.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 850x350 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ch7eStdvOsHd"
      },
      "outputs": [],
      "source": [
        "# Filter and clean\n",
        "temp_df = df_cooking_survey['electricity_payments_repeat'][df_cooking_survey['electricity_payments_repeat']['How many times a month do you make a payment to ${electricity_payment_to_list}?'] != 999]\n",
        "\n",
        "df_aggregated = temp_df.groupby('_submission__id')['How many times a month do you make a payment to ${electricity_payment_to_list}?'].sum().reset_index()\n",
        "\n",
        "# Merge\n",
        "merged_df = pd.merge(df_cooking_survey['Spotlight Kampala Cooking Su...'], df_aggregated, left_on='_id', right_on='_submission__id', how='left')\n",
        "\n",
        "# Step 4: Categorize payment frequencies\n",
        "def categorize_payments(value):\n",
        "    if pd.isna(value):\n",
        "        return 'Unknown'\n",
        "    elif value == 1:\n",
        "        return 'Monthly'\n",
        "    elif value == 2:\n",
        "        return 'Bi-weekly'\n",
        "    elif value >= 3:\n",
        "        return 'Weekly'\n",
        "    else:\n",
        "        return 'Unknown'\n",
        "\n",
        "merged_df['Electricity Payment Frequency'] = merged_df['How many times a month do you make a payment to ${electricity_payment_to_list}?'].apply(categorize_payments)\n",
        "\n",
        "# Step 5: Filter relevant connection types and rename them\n",
        "filtered_df = merged_df[merged_df['Connection type'].isin([2, 5, 6])].copy()\n",
        "filtered_df['Connection type'] = filtered_df['Connection type'].replace({\n",
        "    2: 'Individual metered',\n",
        "    5: 'Collective metered',\n",
        "    6: 'Collective unmetered'\n",
        "})\n",
        "\n",
        "# Define the frequency categories and their display order\n",
        "frequency_categories = ['Daily', 'Weekly', 'Bi-weekly', 'Monthly']\n",
        "\n",
        "# Define custom colors for the categories\n",
        "custom_colors = ['#71B3FE', '#D7CFD6', '#372E2A', '#BC9EA1']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4EZImD_DWr4y"
      },
      "outputs": [],
      "source": [
        "fuel_columns = [\n",
        "    'Electricity Payment Frequency',\n",
        "    'Do you buy charcoal on a daily, weekly, or monthly basis?',\n",
        "    'Do you buy firewood on a daily, weekly, or monthly basis?',\n",
        "    'Do you buy gas on a daily, weekly, or monthly basis?'\n",
        "]\n",
        "\n",
        "fuel_labels = [\n",
        "    'Electricity',\n",
        "    'Charcoal',\n",
        "    'Firewood',\n",
        "    'Gas'\n",
        "]\n",
        "\n",
        "# Payment frequency categories\n",
        "frequency_categories = ['Daily', 'Weekly', 'Bi-weekly', 'Monthly']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kJzvLF5BN5HB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3b924f48-afde-4ef1-b45c-9e5dce777ba8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-508-e73563527219>:57: UserWarning:\n",
            "\n",
            "set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
            "\n",
            "<ipython-input-508-e73563527219>:57: UserWarning:\n",
            "\n",
            "set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define custom colors matching earlier schemes\n",
        "custom_colors = ['#4E79A7', '#F28E2B', '#76B7B2', '#E15759']  # Example of earlier color palette\n",
        "\n",
        "# Update the order for 'Connection type'\n",
        "connection_order = ['Collective unmetered', 'Collective metered', 'Individual metered']\n",
        "\n",
        "# Ensure 'Connection type' is a categorical variable with the specified order\n",
        "filtered_df['Connection type'] = pd.Categorical(\n",
        "    filtered_df['Connection type'],\n",
        "    categories=connection_order,\n",
        "    ordered=True\n",
        ")\n",
        "\n",
        "# Sort the DataFrame by 'Connection type'\n",
        "filtered_df = filtered_df.sort_values('Connection type')\n",
        "\n",
        "filtered_df['Electricity Payment Frequency'] = merged_df['Electricity Payment Frequency']\n",
        "\n",
        "\n",
        "# Function to plot a 100% stacked bar chart for a given column\n",
        "def plot_stacked_bar(data, column, title, ax):\n",
        "    \"\"\"\n",
        "    Plot a horizontal 100% stacked bar chart.\n",
        "\n",
        "    Args:\n",
        "        data (DataFrame): Filtered data containing 'Connection type' and the column to plot.\n",
        "        column (str): Column name to plot.\n",
        "        title (str): Title of the subplot.\n",
        "        ax (Axes): Matplotlib Axes object to draw the plot.\n",
        "    \"\"\"\n",
        "    # Create a crosstab to count occurrences and reindex to ensure all categories are present\n",
        "    counts = pd.crosstab(data['Connection type'], data[column], dropna=False).reindex(\n",
        "        columns=frequency_categories, fill_value=0\n",
        "    )\n",
        "\n",
        "    # Calculate proportions for 100% stacked bar chart\n",
        "    proportions = counts.div(counts.sum(axis=1), axis=0)\n",
        "\n",
        "    # Plot the horizontal 100% stacked bar chart\n",
        "    bottom = np.zeros(len(proportions))\n",
        "    for i, category in enumerate(frequency_categories):\n",
        "        ax.barh(\n",
        "            proportions.index,\n",
        "            proportions[category],\n",
        "            left=bottom,\n",
        "            color=custom_colors[i],\n",
        "            edgecolor='grey',\n",
        "            label=category if ax is axes[0] else \"\"  # Add labels only to the first plot\n",
        "        )\n",
        "        bottom += proportions[category].values\n",
        "\n",
        "    # Set plot aesthetics\n",
        "    ax.set_title(title, fontsize=18, fontweight='bold', pad=10)\n",
        "    ax.set_xticks(np.arange(0, 1.1, 0.2))\n",
        "    ax.set_xticklabels([f'{int(x*100)}%' for x in np.arange(0, 1.1, 0.2)], fontsize=14)\n",
        "    if ax in [axes[0], axes[2]]:  # Set y-tick labels for the first column of plots\n",
        "        ax.set_yticklabels(connection_order, fontsize=14)\n",
        "    else:\n",
        "        ax.set_yticklabels([])\n",
        "        ax.set_ylabel(\"\")\n",
        "    ax.tick_params(axis='x', labelsize=14)\n",
        "    ax.spines['top'].set_visible(False)\n",
        "    ax.spines['right'].set_visible(False)\n",
        "    ax.spines['left'].set_linewidth(0.8)\n",
        "    ax.spines['bottom'].set_linewidth(0.8)\n",
        "\n",
        "# Create subplots\n",
        "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(15, 10))\n",
        "axes = axes.flatten()\n",
        "\n",
        "# Plot for each fuel type\n",
        "for i, (column, label) in enumerate(zip(fuel_columns, fuel_labels)):\n",
        "    plot_stacked_bar(filtered_df, column, label, axes[i])\n",
        "\n",
        "# Add a single legend for all subplots\n",
        "fig.legend(\n",
        "    handles=[plt.Line2D([0], [0], color=custom_colors[i], lw=10) for i in range(len(frequency_categories))],\n",
        "    labels=frequency_categories,\n",
        "    title='Payment frequency',\n",
        "    bbox_to_anchor=(0.9, 0.9),\n",
        "    loc='upper left',\n",
        "    frameon=False,\n",
        "    fontsize=12,\n",
        "    title_fontsize=14\n",
        ")\n",
        "\n",
        "# Add centered x-axis labels for all rows\n",
        "fig.text(0.5, 0.52, 'Proportion of responses', ha='center', fontsize=16)\n",
        "fig.text(0.5, 0.095, 'Proportion of responses', ha='center', fontsize=16)\n",
        "\n",
        "# Adjust layout to ensure spacing\n",
        "plt.subplots_adjust(bottom=0.15, hspace=0.4)\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "45indBHPhj3V"
      },
      "source": [
        "# Awareness of e-cooking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mHkbJVfQOATy"
      },
      "outputs": [],
      "source": [
        "benefits_list = ['What do you see as the benefits of using an electric pressure cooker?/It would save time',\n",
        "                 'What do you see as the benefits of using an electric pressure cooker?/It is cleaner than alternatives',\n",
        "                 'What do you see as the benefits of using an electric pressure cooker?/It would be cheap',\n",
        "                 'What do you see as the benefits of using an electric pressure cooker?/It would be safer than alternatives',\n",
        "                 'What do you see as the benefits of using an electric pressure cooker?/It would be convenient',\n",
        "                 'What do you see as the benefits of using an electric pressure cooker?/I dont see any benefits',\n",
        "                 'What do you see as the benefits of using an electric pressure cooker?/Other']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8u6TCoclqsad"
      },
      "outputs": [],
      "source": [
        "challenges_list = ['What concerns do you have about owning or using an electric pressure cooker?/It would heavily consume electricity',\n",
        "                   'What concerns do you have about owning or using an electric pressure cooker?/Someone would not let me use it',\n",
        "                   'What concerns do you have about owning or using an electric pressure cooker?/I worry for my familys health',\n",
        "                   'What concerns do you have about owning or using an electric pressure cooker?/The wiring is too weak',\n",
        "                   'What concerns do you have about owning or using an electric pressure cooker?/I am worried for the safety of the children in the household',\n",
        "                   'What concerns do you have about owning or using an electric pressure cooker?/Someone might steal it',\n",
        "                   'What concerns do you have about owning or using an electric pressure cooker?/I have no concerns',\n",
        "                   'What concerns do you have about owning or using an electric pressure cooker?/Other']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ovsr14ublOE9"
      },
      "outputs": [],
      "source": [
        "# Clean column headers\n",
        "df_cooking_survey['Spotlight Kampala Cooking Su...'].columns = (\n",
        "    df_cooking_survey['Spotlight Kampala Cooking Su...'].columns.str.replace(\"'\", \"\")\n",
        ")\n",
        "\n",
        "# Extract the relevant DataFrame\n",
        "df_temp = df_cooking_survey['Spotlight Kampala Cooking Su...']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qw0gEyicK3Fo"
      },
      "source": [
        "## Benefits of EPC"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MxvLAqlRK5mO"
      },
      "source": [
        "## Awareness of EPCs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YfnZn1oHneIh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 844
        },
        "outputId": "848a0f70-a5ec-40b2-f305-fe1b2f0d8ab7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'Response': 'No', 'Percentage': 59.17}, {'Response': 'Yes', 'Percentage': 40.83}]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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C08Cie7Trws9RKgAgBrb3St9eJN25Ifx5wGF8wKhjYgGMopFjZDt+/HMN3LHIdRwAwF78QPrzSmldW7j3opJjaYFRxacTMEpsEKiwfYd2fvoLlAoAiLGVu6Rv3SFt6uIaIWA0USyAIo0sfer/1x3a+ekvqLB1m+NEAIDn0z0kXXaPdNPasFywNAooHkuhgCJY35ctFNRxxU80ePdi13EAAAfASrrxMWl9u/S246XqLEujgGLw6QMcJBsEyj+xVbsu/BylAgASbG377qVRnSyNAopBsQAOkA0CSVLfjbdo12e/pMKOnY4TAQCK1TssXX6vdMu68OcsjQIOHEuhgANgfV82l1P7ZVdq6P4HXccBAIyiwEr/WCNt6JDedpxUwalRwAHh0wXYTzYIlN+0WTs/+TlKBQCUsdWt0ncWSe0D0u4hNYD9QLEA9tPg4vu064v/I7+11XUUAECJtQ1I310UHk3Lvgtg/1AsgOcwcpRs9zXXqeP7l0n5vONEAICoDPvSzx+Ubl4b/px9F8BzY48FsA/W96UgUPsPf6TBe+93HQcA4ICV9M/HpG290psXStaw7wLYF4oF8Cys7yvo7VPbxf+r/PoNruMAABxbvl1q7ZPec5LUUEW5AJ4NnxbA01g/UH7LE9r56S9QKgAAe2zvlb59p7SR+y6AZ0WxAPZirdXgg0vU+oWvKujsdB0HABAz/XnpR/dKizaGP6dgAE9iKRSgsFAYY9R7/Q3qufYPvFIAAPYpsNKfVkjbe6TXHRO+ZGSM61SAexQLpJ71fclatV92pQbvXuw6DgAgIe7bInUPSe84QfLY1A2wFArpZn1fwcCAWr98EaUCAHDAVrdKP7xbGsxLPpfpIeUoFkgt6/sqbNuuXZ/+gnJr17mOAwBIqK090nfvkjoGKBdIN4oFUslaq6FlD2vX574sv63ddRwAQMJ1Dkrfv1va3MVFekgvigVSqf/mW9V+yXdlh4ddRwEAlImB3SdGLd/OGSBIJ4oFUqfnj39W109+wVd9AMCoKwTS1Uul29e7TgJEj2KBVOn+zf+p53fXuo4BAChjVtJfVknXPxp+D4ulUUgLjptF2bO7JxNdP/mF+m++1XEaAEBa3LUxPI72bcdJgbjrAuWPiQXKmg0CyVp1XnoFpQIAELlHdkg/eSA8LYrJBcodxQJlywaBFARqv+R7Glh0t+s4AICUWtMqXXlfuP8i4DhalDGKBcqS9X2pUFDb/7tEQ0uWuo4DAEi59R3S5YulnM9dFyhfFAuUHev7srmcWr/6dQ0/usJ1HAAAJIV3XFx6jzRUoFygPFEsUFas7yvoH1DrF/9HubWPu44DAMBTbOuVfnh3eOcF5QLlhmKBsmF9X353t1q/8BXlN29xHQcAgGe1qz+8pbt3mHKB8kKxQFmwvi+/tU2tn/+qCjt2uo4DAMBz6hgIy0XnIOUC5YNigcSzvq/C1m3a9cWvym9vdx0HAID90j0k/eBuqbWfcoHyQLFAolnfV37LE9r1pa8p6O5xHQcAgAPSlws3dLdRLlAGKBZILOv7KuzYqbb/+YbswIDrOAAAHJSBvHTZYpZFIfkoFkgk6/vy2zvU+tX/p6Cvz3UcAACK0pcLy0X3EOUCyUWxQOJY31fQ26vWr35dQVeX6zgAAIyK7qGwXPTlKBdIJooFEsX6vuzgoFq/8nX5rW2u4wAAMKo6B8M9F9xzgSSiWCAxnrxR+/+psG276zgAAJRE+4B0GTd0I4EoFkgEGwSyvq+2i76p/KbNruMAAFBSu/qlyxdLOV8KKBdICIoFYs8GgRQEar/428qtXec6DgAAkdjeG5aLfCAF1nUa4PlRLBBr1oZfSdu//X0NP7rCcRoAAKK1tUf60b1SgXKBBKBYILZGSkXHD3+koSXLHKcBAMCNzV3Szx6QZCVLuUCMUSwQW8YYdV31Uw3evdh1FAAAnHqsTfrdw5IxrpMA+0axQGx1/fLX6r/1dtcxAACIhSVbpb+scp0C2DeKBWKp+9o/qO9v/3QdAwCAWLntcWnRBpZEIZ4oFogVa636/nmTen//R9dRAACIpT+tkJbvYDM34odigdiwvq/h5Y+o6xe/dh0FAIDYspJ+s0za1MkFeogXigViwfq+Cjt3qf07P+QmIAAAnkchkH7yQHhLN+UCcUGxgHM2CGQHh9T29W/JDg66jgMAQCIM5qUr7pX6c5QLxAPFAk5Za6UgUNs3/1d+a6vrOAAAJErXkHTlfVygh3igWMApY4w6f/Rj5dasdR0FAIBE2t4r/fxB1ykAigUcstaq5/obNLDobtdRAABItMfapBtWuE6BtKNYwAkbBBq8/0H1/N/vXUcBAKAsLNoo3buZJVFwh2KByFnfV37TFnX+8Efc8AMAwCj6wyMcQwt3KBaIlPV9Bb29arv4EtlcznUcAADKim/D/RZ9nBQFBygWiIy1VvJ9tX3jfxV0drmOAwBAWerLST++L1wSxbIoRIligcgYY9T+/cuV37DRdRQAAMratl7p18ukjHGdBGlCsUBkun/zfxp6gPPwAACIwiM7pH+scZ0CaUKxQMnZIFD/7YvUe8NfXEcBACBVbl4rPbyNJVGIBsUCJWV9X/nNW9R51U9dRwEAIJV++5C0s5fN3Cg9igVKxgaBbC6n9ku+JxUKruMAAJBK+SA8KcoPmFygtCgWKBmTyajjBz+S39rqOgoAAKnWNiD99mE2c6O0KBYoCWutem/4q4aWLHUdBQAASFq+XbpzA1MLlA7FAqPO+r5yj61V9zXXuY4CAAD28peV0tZu9lugNIy1lt6KUWODQEF/v3Z+8rNcggfEUSYj09CgbEuTvKYmmTFj5DU2yGtokKmvU6auVqaiUiaTkYyRMhlVHjlHA0GFtvZI1j556VYhkHK+lCuEF3L1DIUfnUNS95A0VOA7o0AcNddIF54lVWZZGoXRRbHAqLLWqu1rF2v40RWuowDp4nmqmDFNFdOnK3vIFGUnTpA3dqy8MY3K1NfLVFZInidjnvtdxL5fEp79v3ue325PARkuSIN5qT8n9QxLXYNS52B4Us2GzrCgAIjOUROkd5/kOgXKDcUCo8Zaq97f/1E9113vOgpQnoxRdtZMVc+fp8rDDlXF5EnKNDUpU1cbTiL2epe/95f25ysTUdn71WbvSCNTkP5cWDh29EmbuqR1bVJbv8SLFFAaL58rnXPo83+DANhfFAuMCuv7yq19XK1fuUgKWLgJFCszZoxqTjxeVfOOUsXM6fLGjZWprNxTEuJYHIo18n/p6aUj54fTjW090uPt0oqd4dQDQHEyRvrwqdK0Jslj1y1GAcUCRbNBIDs4pJ2f/Iz89g7XcYDEMbW1qj3tZFUdu1CVs2bIa26W8TxJTxaIcikPB+vppSMIwnKxpVtauVN6eHu4pwPAgRlTHe63qK5gvwWKR7HAqGj71nc09CBHywL7I9PcrLpzzlL1cQtVMX2aTFWVjDGUiAO0d9mwNtzHsbVHenSH9OATUn/ebT4gKeaOl957susUKAcUCxTFBoH6b7pFXT/7lesoQGxlxo5V/XkvVPWC+cpOmypTUbGnSFAiRs8zioYfLp9asUO6/4lwDweAZ/eao6VTZzC1QHEoFjho1vdV2LpNOz/7JSnPtwaBvVWfeLzqznuRquYcIVNdRZFw4OlFI+dLm7vCacaDT7ApHNhbRUa68OzwKFr2W+BgUSxwUKy1Uj6vnRd+XoXt213HAdyrqlL9i89T7emnqmLaVBnPY2lTzOxdNAIr7eiVljwhLdogFXglBDRtjPSxM5ha4OBRLHDQOq/8ifpvvd11DMAZb+IENbziZao+fqG8lhamEglj7ZPTjPYB6aFt0h0bWDKFdDvvcOnFR3AELQ4OxQIHzPq+hleuUtvXLnYdBYhebY3GvPrfVXvW6cqMGUOZKBN7l4zOQemBLdJt67m4D+mTMdJHT5cOaWRJFA4cxQIHxForm8tp5yc+Jb+t3XUcIBqZjOouOFf155+r7JTJlIkyt3fJ2NUn3btFumuD5PNqiZQYVxvut8hmmFzgwFAscMA6f/xz9d98q+sYQMlVH7dQDa96pSoPP0wmk6FMpNDeJWNrj3TzWumRHa5TAaV3ynTp9ce4ToGkoVhgv1nfV27NWrV+9etP7oIEyoypr1PT296smlNOVqa6ijKBPUZKxlBBun+L9PfVLJVCeXv3idKc8SyJwv6jWGC/jJwCteO/Pi1/V6vrOMCoqzxyrpre+iZVHDqbpU54TntPMdZ3SH9eGd4ADpSb+krpMy/gVm7sP4oF9lvXz3+lvn/c5DoGMHqMUcO/vUL1L7tAmcbG3b/Eqyf238graM9QuNl70Qbux0B5mT9JeucJrlMgKSgWeF7W95Vb97hav/Q1lkChLGQaG9X0n29XzUkn7LlvgkKBYoxMMQqB9PB26fpHpUHuDUWZeOfx0lETWRKF50exwPOy+bx2fvKzKmxnxyKSLTt9qprf/S5Vzjl8z69RKDCanr5M6g+PhhfxAUnWWCV99hyp0uOUKDw3igWek7VW3b/+nfr+8nfXUYCDVnHEYWp+73+qYtpUSZQJRGPk1XVXn3TNcmlTp9s8QDE4JQr7g2KBfbK+r/zGTdr1uS+zBAqJVLVgvpre9XZlJ02URKGAG3sXjGsfkTZ0uM0DHAwj6cOnSdObWBKFfaNYYJ9soaCdF35Oha3bXEcBDkjl0fPU8oF3yxs3ThKFAvEw8mrb2if95iFOkkLyjK+TPnU2xQL7RrHAs7LWqud316r3T39xHQXYbxWzZ6nlw+9X9pApkigUiKeRV90t3dJvlkmt/W7zAAfi3MOkl8xhrwWeHcUCz2B9X/ktT2jXZ74oBYHrOMDz8iZN1NiPf1gVs2ZKolAgGUZefde1S79eKvXm3OYB9odnpE+eJY2rY3KBZ6JY4Bms72vnpz6vwpYnXEcBnltNjcZ+7IOqPnahJAoFkmnkVfiRHdLvHpKGuc0bMTe9SfrY6Uwt8EwUCzyFDQL1/vHP6vm/37uOAuybMRrz9jer/oLzpEyGQoHEG3kltpLu3xweU+vz6owY+/ejpDNmcSM3nopigT1sECjo6dGOj/y37PCw6zjAs6o+5SS1vP/dytTWcrEdys7IPRg5X7puubRkq+tEwLOr9MK7LRqqKBd4EsUCT9H+/cs0ePdi1zGAZ/AmTNDYCz+uiunTJLHsCeVt5JV5e6/04/ul7iG3eYBns2Cy9PbjXadAnFAsICncV5Fbv0Gtn/+K6yjAU2Wzav7Ae1R7xmmSKBRIl5FX6Ls3Sn9cES6VAuLkQ6dKM5vZyI0QxQKSwuNld33mi8pv2Og6CrBHzemnqvn971amqoplT0itkeVRA/nw9KjVra4TAU+a3CD991ksh0KIYgFZ39fAHXep84ofu44CSJJMba3Gff7TqjxsdvhzCgWwZ3qxvkP68X1SjtPAEROvPlo6bQblAhQLSAqGhrXjI/+loJtrYOFe3bnnqOldb5c8j0IBPAtrJT+Qfv+IdD+ngiMGaiukz79Qqs5yBG3aUSxSzlqr7l//Tn1/+bvrKEi5TH29xn3h01xyB+yHkVfuJ7qlq+6T+vNu8wCnzZBeO991CrhGsUgxGwTyW9u04+MXSj43MsGdugvOVdM73sqdFMABslYKrPT31dJt612nQZoZhTdyT6yXMmzkTi2KRcq1Xfy/Glr6kOsYSKlMQ4PGffEzqpw5g83ZwEEa2dy9vUe6fDHTC7gzu0X68GmuU8AlOmVKWd/X0MOPUCrgTM3pp2rylZeqYsZ0SSx9Ag7WyKfOpAbpK+dJC6e4zYP0Wt8hLdsW7gFCOlEsUqzrF1e7joCUavnER9Ty0Q9KHkufgNFiTHgqz1uP5dIyuPOXleHyPKQTxSKFrO+r7583qbB1m+soSJnM+HGadMUPVXvqyTLGUCqAUWZM+LFgsvTV86Sxta4TIW26hqR/PU65SCuKRcpYa2UHh9Rz3R9dR0HK1J5ztib/4NvymptcRwFSob5S+uw54Wk9QJRuf1wayj95ehnSg2KRMsYYdf/mGtmBAddRkBbGaOyn/0vN7383pz4BETImPKnnNUdL7zmRF3xEZ9iXblzrOgVc4OtMiljfV37zFvX/63bXUZAS3sQJmnzVZao5/jiWPgEOjCyNOnJiuLGbpVGIyj2bpJ5hphZpQ7FIEeN56v7dtXyWIxI1p56sSd/7X2UaG1xHASCprlL67AukBZNcJ0Ea+IH0t9XcxJ02FIuUsL6v3IaNGlqyzHUUpMCYN79RLR//sJRhSgHExcj04m3HS68+2nUapMGSJ6SdfVLA8bOpQbFICeN56rnm965jIAXGffEzavi3l0vibgogbkY+Jc+YKX30tHAPBlAqVtJfV3ETd5rwUKeA9X3lHt+goWUPuY6CMmbq6zTp8u+ren74rVBKBRBPI5+aM1ukL75Iqq1wmwflbcVOaVMnl+alBcUiBYznqef/rnMdA2WsYtYsTf7RD+WNbXEdBcABGFMtfelF0hS2QqGE/rJK8njHmQo8zGXO+r5y6x7X0EPLXUdBmao983RN+MZXZSormFIACWOMVOFJ/3WWNH+i6zQoV+s7pNW7mFqkAcWizBnPU/c1TCtQGg2ve42aP/x+iaNkgcQaue/iHSeEey+AUvjraqYWacBDXMas72t47ToNL3/UdRSUoeYPv1+Nr/13SeynAJJu5FP4VfOkVx7pNgvK07Ye6eHtTC3KHcWijIUnQTGtwOgb+7lPqe6sM7j0DigjI5/KLzhUevvxbrOgPN28lqlFuePhLVPW9zW8Zq2GH1nhOgrKiTGacPH/qGbhMa6TACiBkXKxYLL0sdM5jhaja1uPtIq9FmWNYlGmmFZg1GWzmvi9S1Q5e7brJAAiMKNZ+twLpUreKWAU3fQYU4tyxkNbhqzva3j1Gg2vWOk6CsqEqa3R5Mu/p+wkjo0B0qSlRvrSuVJ9leskKBebuqR1bUwtyhXFogxxyzZGk6mv16RLv6vMmDHspwBSxhippkL67Auk+krXaVAubmKvRdniYS0z1vc1vHK1hleuch0FZSDT3KTJl35Hmbo6SgWQUsZI1Vnps+dIddzSjVGwrl3a3MXUohxRLMqM8Tx1c8s2RkFmbIsmff9/ZWpqKBVAyo2Ui8+9UKplcoFRcCN7LcoSD2kZGZlW5FatcR0FCZdpGqNJ3/2WTFUVpQKApL3KxQukmqzrNEi6Vbuk7T1SYF0nwWiiWJQR43nq/fNfXcdAwpn6ek363iWUCgDPMLLn4nMvDEsGUIyb1koZXmbKCsWiTFhrVdjVqqFlD7uOggQztTWa9AOWPwHYN2Ok2opwz0WV5zoNkmz5dqm1j6lFOaFYlJG+v/1Dsnx24iBls5r0vUvYqA3geRkTnhL16RdIHl8ucJCspFsfZ2pRTigWZcIO59R/+yLXMZBUxmjidy7mSFkA+80YqalGuvBsbujGwVu6VRrIuU6B0UKxKAPW99V/622yg4OuoyChJnzjf1QxaRKlAsABm1AvfewM1ymQVIVAunsTy6HKBcWiHGQy6vvnTa5TIKHGfeHTqpw9y3UMAAk2vUl638muUyCp7t6ocF0UEo9ikXDW9zW09CH5O3e5joIEav7I+1V9zHzXMQCUgTnjpbcc6zoFkqhnWHpoOxfmlQOKRcIZz1Pf3/7pOgYSqOF1r1btGae7jgGgjBw7RTr/cNcpkER3buDCvHLAQ5hgNgiUf2Krhh9d4ToKEqb2jNPU+NpXuY4BoAxdcERYMIADsbkr/GCvRbJRLJLMmPCIWeAAVMyaqeYPv1+S2KwNYFSNfEl587HS5Aa3WZA8d6zn6Nmko1gkmB0Y1MCie1zHQIKY+jqN/58vSsZQKgCUhDHh8bMfPZ3buXFgHt4u9Q5zJVeSUSwSyvq++m66RTbH4c/YfxO/9f9kKispFQBKyhip0pP+60zXSZAkgZUWbeCAqCSjWCSVMeq/6RbXKZAg4774WWXHjaVUAIiEMdK4OuldJ7hOgiRZvJl9FklGsUgg6/savO8B+e0drqMgIca85U2qnj/PdQwAKXT0JOncw1ynQFL056SlT3D0bFJRLBKII2ZxIGpOO0X1r3ipLItWAThgrfSSOeE9F8D+uHcLR88mFQ9bwtggUG7DRuUeW+s6ChIgO2miWj7yQUmcAAXAjZEvPe8+UWqudpsFybCxU2rtZxN3ElEskoa9FdhfmYzGX/QVKcMJUADcMiY8RvTjZ/LGA/tn8SY2cScRn99J4/saWHy/6xRIgLGf+aQyDfWUCgCxYIzUUCW99xTXSZAEDz4hmkUCUSwSxPq+Bh9YIjsw4DoKYq7ugnNVvWA+pQJA7BwxTjpzpusUiLu+nLRyF5u4k4ZikSDG8zRw+yLXMRBz3uRJanrn21zHAIBnZa30b/OklhrXSRB3925mE3fS8HAliN/bp6Hlj7iOgTgzRhO4WRtAjI3czP2R01wnQdytbg1v4kZyUCwSwvq+Bu68S/J911EQYy3//VF5Y8ZQKgDEmjHSmBrpTQtcJ0GcBVa6b7MUsBwqMSgWCWE8LywWwD7UnHm6ak7kilsAyXHCVGku91vgOdy/RcrwbjUxeKgSwFqr/NZtym/Y6DoKYirT0KCWD77XdQwAOGDvPEGq5N0I9qFtQFrfwdQiKfhUTgJr1X/bHa5TIMbGfeEzUibDEigAiWKMlM1I7znZdRLE2b2bmFokBQ9TEhijgUX3uE6BmKo7/1xVzJxOqQCQSMZIs1ukEw5xnQRxtXyHlGeLaSJQLGLO+r6GV6xU0NnpOgpiKFNfr6Z3vNV1DAAo2usXSNVZ1ykQRzlfWrGTOy2SgGIRc8bzNHDbna5jIKbGff7TkscSKADJZozkGen93MqNfVi2jTstkoCHKOZsLqfB+x90HQMxVHfuOaqYPZNSAaAsGCNNGyOdMcN1EsTR6l1SruA6BZ4PxSLGbKGggcX3yQ5zOwyeytTVqeldb3cdAwBG3b/NkxoqXadA3OSDcK8Fy6HijWIRYyab5e4KPKtxn/+U5HlMKwCUFWOkjJHexylReBYPsRwq9nh4Yszv6tLwoytdx0DM1JxxmioPnU2pAFCWjJEmN0oLJ7tOgrhZ0yoNsRwq1igWMWV9XwN3LJKsdR0FceJ5an7fu12nAICSe/0CiW+fYG++lR7exnKoOKNYxJTxPDZt4xmaP/AeZaoqmVYAKGvGhEfPvvpo10kQN5wOFW88NDHl9/Ypt2696xiIkcyE8ao983RZplgAUsBa6dQZUiMbubGXde3SQN51CuwLxSKGbKGgoQceZBkUnmLchZ+QJKYVAFLBmHAp1LtOcp0EcRLYcBM3y6HiiWIRQyab1eCSZa5jIEaqTz5RFdOnUSoApMrI3RZzx7tOgjjhdKj44mGJIVsoaHj5o65jIC6MUcsH3uM6BQA48+Zj2ciNJ63vkAZZDhVLFIuYsb6voUdWcCke9hjz9jcrU1vLtAJAKhkj1VVKL5vrOgniIrDSip0sh4ojikXcZDIaenCJ6xSIi5oa1b/4fDZsA0g1a6WzZ0uVnuskiItHd7IcKo54SGLGGKOhJQ+5joGYGPuxD0nGMK0AkGojN3K/eaHrJIiLNbuYWMQRxSJGrLXKbdosv6PDdRTEgDdpkqqPXUCpAIDdjp4kja11nQJxMOyHR88GDPRjhWIRJ0GgIS7Fw24tH/2g6wgAEBsj32N5+/FucyA+Ht3hOgGejmIRI8bzNPjgUtcxEAPZGTNUeegsphUAsBdjpEMapelNrpMgDlbtCpfIIT4oFjHid3crv2Gj6xiIgbEfeb/rCAAQW29c4DoB4qBjUGrtc50Ce6NYxIQtFDR4P6dBQaqcO0fZaVOZVgDAszBGmlgvzWx2nQRx8CjHzsYKxSImTDaroSUsg4LUzGV4APC8mFpACpdDcexsfPBQxITN5zX0yArXMeBY5VFzlZ00kWkFADwHY6TxddKMJtdJ4NqGDinnu06BERSLGLC+r6GHH5Hy3E+fds3vfpfrCACQGK+d7zoBXPOttKZVClgOFQsUizjIZFgGBVUcNlvZQyYzrQCA/WCMNKUx3G+BdFvb9uRxxHCLYhEDxhiWQUHN72dvBQAcqNcf4zoBXKNYxAfFIgb87m75u1pdx4BD2WnTVMFJUABwQIwJT4dqqnadBC7t7JMGcq5TQKJYOGd9X8OPrnQdA441v/sdriMAQGK9gROiUu+xNo6djQOKhWsmo+FVa1yngEOmoUGVc49gWgEAB8EY6YhxUm2F6yRwaV07t3DHAcXCMZMxGl612nUMONT8n+9wHQEAEu917LVItXXt7LOIA4qFY8HAoApPbHUdA64Yo5qTT3CdAgASb/4kyeONZWrt6pP6hl2nAMXCIRsE4bTCWtdR4EjDv71CxvNYBgUARTAmXAbz0jmuk8ClteyzcI5i4djwSpZBpVn9yy6QpVgCQNGslU6a7joFXGKfhXsUC4dMJqPcaopFWlXOO1KZxkamFQAwCoyR6iql2S2uk8AV9lm4R7FwyOZyyq3f6DoGHGl6y5tcRwCAsmKt9LK5rlPAldZ+qZd9Fk5RLByxQaDhtY9Lvu86ChwwDQ2qmD2LaQUAjKKRC/MqeHeTWuyzcItPPVesVW7lKtcp4EjTW95EqQCAEjBGejGbuFNrYyfLoVyiWDhiPI+N2ylWc+pJbNoGgBKwVjp5musUcGVzFxu4XaJYOGJ9X7m161zHgANVCxcoU13NxAIASsAYqZZN3Km1tYelUC5RLByw1iq/cZNsLuc6ChxofPUrmVYAQAlZK51/uOsUcMEPpB29rlOkF8XCBd/X8Ar2V6SSMao84nCmFQBQQsZIh46V+EqbThs6pQJTCycoFg6YbDa8cRupU3fBeTIZPu0AoNS8jHTqDNcp4MLmLsmjVTrBOxxHco+xvyKN6i84l2VQABABa6UzZ7pOARc2d3EylCsUCwf8rm4FvSwATJ3aGmWnTGYZFABEwBhpQr1UW+E6CaLW2iflCq5TpBPFImI2CJR7fL3rGHBgzGteRakAgAgZI11whOsUiJpVOLVggUD0KBZRCwLlN25ynQIO1J55OsugACBC1krHTnGdAi5s6pICXnIjR7GImMlmKRYplJkwXpkxjUwsACBCxkh1lVJzjeskiNrmrnADP6LFX7kDOYpF6jS84mWUCgBwwBjpnENdp0DUNne5TpBOFIuIBUND8ne1uo6BiNUcfyzLoADAAWuloye6ToGodQ9JQ3nXKdKHYhEha63ymzazmyhtqqrkjW1hYgEADhgjjamWqjzXSRA1buCOHsUiSr6v3PoNrlMgYvXnn0upAACHjJHOnOU6BaK2tYcbuKNGsYiS5ym/aYvrFIhY7ZmnsgwKABzidKh02tknZfi+XqQoFhEyxqiw5QnXMRCximnTmFgAgEPGSBMbJL4Sp8uOXopF1CgWEcs/sdV1BESo+sTjZTwW9gKAaxkjzZ/sOgWixB6L6FEsIuR3dckODrqOgQjVn38uy6AAIAaslU6Z5joFotSXkwY4GSpSFIuIhCdCsb8ibSrnHO46AgBgt1ktrhMgajt6OIwzShSLqPi+8pspFmnijRsnU1XF/goAiAFjpEpPaqhynQRR2tYr+RSLyFAsouJ5yj/Bxu00qX3h2ZQKAIgRY6TTZ7hOgSjt6JU8XoojQ7GIiDFG+c0UizSpWXgM+ysAIEasleZxC3eq7OgNCyWiQbGIUGHrNtcREKEsx8wCQKyMHDuL9OBkqGhRLCLi9/bJDg25joGIZJqaZCorXMcAADxNNiONq3WdAlEZyEuDnAwVGYpFRPy2NtcREKG6c9hfAQBxddwhrhMgSp2c9B8ZikUEbBCosHOX6xiIUPXxC9lfAQAxZK00d7zrFIhSa78U8JIcCYpFFIJAfisTizSpmM7+CgCIq0nss0iVjgGKRVQoFlHIZOS3tbtOgYiYujqZKg5KB4A4MkaqyoZ3WiAdOgY4cjYqFIsImExGBfZYpEbtaSczrQCAGDNGOmqC6xSISscgR85GhWIRESYW6VF9LPsrACDujqRYpEb7gOsE6UGxiAh7LNKjYuZ01xEAAM/BWml6k+sUiEonxSIyFIsI2FxOQV+f6xiIiNfczFIoAIgxY6QW7rJIjXwg9edcp0gHikUECu0driMgIpmWZhmPHYEAEHfZjFSddZ0CUWE5VDQoFiVmrZW/q9V1DESk5qQTXEcAAOwHY6T5k1ynQFTauMsiEhSLUvN9FVopFmlRdfRRbNwGgATgorx04S6LaFAsSs6wcTtFKqezcRsAkmJqk+sEiErXEHdZRIFiUWIm63HUbIpkWti4DQBJYIzUXO06BaLSO8xdFlGgWESAy/FSIpORqahwnQIAsJ+8DN/FTou+YdcJ0oFiEQEmFulQcdihTCsAIEGMYTlUWvRx3GwkKBYlZoNAfken6xiIQPXRR7mOAAA4QLOaXCdAFHqZWESCYlFiQW+f5PuuYyAClbNncSIUACSItdKUMa5TIApDBckPXKcofxSLEuPG7fTwJk5wHQEAcIDG17lOgKhw+3bpUSxKLOjvdx0BEfGam1xHAAAcoDE1rhMgKuyzKD2KRQlZa8OlUEiFTG0dm7cBIEGMkeo5zC81uofC5W8oHYpFKQWBgoEB1ykQBc8Lzy0EACQKR86mR+8wt2+XGu+ESslaWZZCpULF9GlMKwAggYyRxrHPIhX6hiV6RWlRLEosGBh0HQERqJg5w3UEAMBBOqTRdQJEoTfHG99S4++3lIxR0M9SqDTIHjLFdQQAwEGa2OA6AaLQPyxleOdbUvz1llImI8sei1TITpzAHRYAkEDWcuRsWgxzrVjJUSxKyBjD5u2U8MaOdR0BAHCQmjlyNhWGC64TlD+KRYlRLNLBG8MCXQBIqoYq1wkQBYpF6VEsSsyyxyIVMnXcYQEASWSMVMtdFqmQYylUyVEsSoyJRTqYSl6VACCpKjzXCRAFJhalR7EoMU6FSgmPVyUASKoMA+dUYPN26VEsSoyJRQpkMiyDAoAEM4ZykQY5JhYlR7EoIVsoSPm86xgoMdPAAegAkHTVWdcJUGr5QAo4Gb6kKBYlZIeGXEdABLItTa4jAACKxMlQ6ZBnOVRJUSxKKBgYdB0BEfCamlxHAAAUqZ5ikQqcDFVaFIsSYmKRDmbMGNcRAABFquNwv1Rgn0VpUSxKyFoW8qWB18geCwBIujomFqnAyVClRbEAikSxAIDkY2KRDmzeLi2KRSkxsUgFU1/vOgIAoEg1FItUoFiUFsWilCgWqZCpqWHZGwAkXC3FIhV4uS4tigVQJJPl8HMASLoqvpSnAr2itCgWpcSzNx24rhUAEs1aKeu5ToEoMLEoLYpFSfHsTQVDsQCApKvgS3kq8M6stCgWQLEoFgCQeEws0oGJRWlRLEqJJ29KUCwAIOl4w5kOPM6lRbEoKZ69AAAkgc9LdioErgOUOYoFAABIvYB3nKnAxKK0KBalxLM3HXicASDxCnwpTwVeskuLYlFKPHkBAIg9YySfiUUqcPN2aVEsgGLx7Q8ASDyKRTp4vPMtKf56S8jyhjMVbG7YdQQAQJGGC64TIAqVHCtcUhQLoEhB34AMd1kAQKIN+64TIAoVFIuSoliUFBOLNAj6+1xHAAAUaTDvOgGiUME735Lir7eEjJd1HQERCHooFgCQdBSLdGBiUVoUixIytTWuIyACfm+v6wgAgCJRLNKBYlFaFIsSytRUu46ACNjubtcRAABFGqBYpEKWd74lxV9vCZkaJhZp4FMsACDxBnKuEyAKFIvS4q+3hDJVVeGtOyhrhY4u1xEAAEViYpEO3GNRWvz1lpipZjlUubPssQCAxOtjYlH2Mib8QOlQLEosw3Ko8hcEXIYIAAlmrTTEBXllj6NmS4+/4hLjZKiU8APXCQAAB8nne0OpkOVEqJKjWJQYE4t0sLlh1xEAAAdpmGlFKlRRLEqOYlFiTCzSIejrZzkUACSQtVI/+ytSobbSdYLyR7EoMSYW6cCRswCQXL0MnVOhtsJ1gvJHsSgxU1vrOgIi4Ld3uI4AADgIxkidg65TIAp1TCxKjmJRQtb3mVikRGHnLhnuLAGARGrvd50AUaitCJe+oXQoFqVkrTLssUiFwrbtriMAAA7STopFKtRVSgHFoqQoFiVmmFikQn7TZtcRAAAHaXuP6wSIQm2FRK8oLYpFibEUKh3ymzZzKhQAJJC1UhsTi1SorZRYtFxaFItSymQ4bjYtfF8KuCQPAJLGt1yQlxZ1lZLHO9+S4q+3hEwmo0xdnesYiEgwMOA6AgDgAFgrDXCHRWrUcypUyVEsSsxrbnIdAREJOrtYDgUACcMdFunBcbOlR7EoMW9si+sIiEh+2zbXEQAAB6idYXNqcEFe6VEsSixTWytTSUVOg9za9dxlAQAJYoy0jROhUiFjpKqs6xTlj2IRAW/sWNcREIGhRx51HQEAcIDWtLlOgCg0VoVFEqVFsYiAN47lUGlQ4MhZAEgUa6XNna5TIApNHNIZCYpFBJhYpIS1sjmOFwGApMj7XJiWFhSLaFAsSswWCvLGUSzSwm9rZ2oBAAlgrdQx6DoFotJczXVTUaBYRCDLxCI18ps2u44AANgPxkhbulynQFSaaiR6RelRLErN8+SNp1ikxfCKlZwMBQAJsbrVdQJEpakmPBkKpUWxKDFjjLzx413HQEQGH1jqOgIAYD9YK63c6ToFotJSS7GIAsUiAl5Ls+sIiEjQ1SXr+65jAACeRyGQhvlynRpN1a4TpAPFIgKZqiqZulrXMRARv6OTDdwAEGPWSh3cuJ0aXkaq467iSFAsIsIG7vTIbdjoOgIA4Hk80e06AaLCtCI6FIuIcJdFegwtfYgN3AAQY8ZIa9i4nRrcYREdikUErLXcvp0ig/fez1IoAIgxa6VH2bidGkwsokOxiILvM7FIETswIDs87DoGAGAf8oE0VHCdAlEZXyf5XGIRCYpFFIzh9u2UyW/ewtQCAGLIWqmtz3UKRGlCvcQC5WhQLCJgPE/ZCdxlkSZDy9hnAQBxZIy0iv0VqTKpQcrwjjcS/DVHJHvIIa4jIEL9ty1iYgEAMWStdM9G1ykQFSNpXJ3rFOlBsYiI11CvTGOj6xiISNDRIZvPu44BANiLtVLelzqHXCdBVJpqpCzvdiPDX3WEKqZNdR0BESo8sZWpBQDEzNYe1wkQpQn1rhOkC8UiIjYIVDGdYpEmQw8/wj4LAIgRY6SHt7tOgShNqJMCvscXGYpFVIJA2enTXKdAhPpuupWJBQDEiLXSA1tcp0CUJtRTLKJEsYiIyWZVOWOG6xiIUNDeLjs8TLkAgBiwVhrMS4PcX5EqE+slj8UDkaFYRCg7jZOh0ia3dp3rCACA3da1u06AqE1sCJfAIRoUiwhlqqrkjR/nOgYi1H/r7eyzAIAYMEa6e5PrFIhSlSc1VLlOkS4Ui4hxMlS6DC6+TzYIXMcAgNTzA2ltm+sUiNJ4ToSKHMUiQtb3VcEG7nSxVoWt29hnAQAOWStt55jZ1JnSGD72iA7FImJMLNJn4J57WQ4FAA4ZIy3jmNnUmTpG8ikWkaJYRMh4nipmzXQdAxHr+8dNTCwAwCFrpXs2uk6BqE0bw63bUeOvO2LZyZMkz3MdAxGyAwPyOzspFwDggLVS77A07LtOgigZhUuhEC2KRcSM5yk7aaLrGIjY0NKHWA4FAA4YI63c5ToFojahXqrg+7iRo1g4wAbu9On505+ZWACAA9ZKt6x1nQJRmzrGdYJ0olhEzBYKFIsUCna1KejqplwAQIRGlkF1DLpOgqgdMkYqcNp75CgWUctkOBkqpfrvvIvlUAAQIWOkpVtdp4AL08ZIHi+5kaNYRMxkMqo8bLbrGHCg5/obmFgAQISslW5mGVTqGIVLofheXvQoFg54LS3yWlpcx0DUBgdV2MZleQAQBWultn5psOA6CaLWUitVZV2nSCeKhSOVcw53HQEO9N14K8uhACACxkiLN7tOARfYuO0OxcIBWyioau4c1zHgQP9Nt8gG7CYDgFILrLRog+sUcGHqGMnnpdYJioUDJptV1bwjXceAC0Gg3Np1LIcCgBKyVtrcJfl8qU2lWc1ShsUBTlAsHMlOPUSmpsZ1DDjQ+8e/sBwKAErIGO6uSCsvI01vZuO2KxQLR0wmo8ojDnMdAw4MLV2mYHiYqQUAlIC10nCB27bTatoYKcu7W2f4q3fE+r6q5hzhOgYcGbx7MVMLACgB7q5It1kt4f4auEGxcCWTUdVRc12ngCNdV/+WiQUAlIC10t9Wu04BVw7lNH+nKBaOGGNUefhhkue5jgIHbP+A8o+vp1wAwCiyVtreKw3kXSeBC0bS7BY2brtEsXDIVFSoYuYM1zHgSNfVv3UdAQDKijHSP9a4TgFXJjVI1RWuU6QbxcIhGwTcZ5FiuVVrFPT0MLUAgFFgrdSfk1bsdJ0ErsxqCZ8HcIdi4VjVkRSLNOv7241s4gaAUWCMdMd61yng0mw2bjtHsXDIsIE79Xpv+Ius7zO1AIAiWBvetHzrOtdJ4NJhY8N7LOAOf/2OZerrlZ08yXUMuGKtBu9f4joFACSaMdKybRLfokmv5hqpsdp1ClAsHLPWqnIu91mkWdfPfuk6AgAkWmCl6x91nQIuzeaY2VigWLjm+2zgTrmgu1u5x9ayHAoADoK10upd0lDBdRK4dPi4cDkc3KJYOGayWVXNO8p1DDjWedXPXEcAgMS6brnrBHDtqAnsr4gDHoIYyE4YL2/8eNcx4FBhyxPKb9rM1AIADoC10qZOqXvYdRK4NKVBqq9ynQISxSIWbBCo+tgFrmPAsc4rfuo6AgAkzrVMK1Jv7gSOmY0LikVMVB+30HUEOJZfv16FrduYWgDAfrBW2tEXfiDdjpzgOgFGUCxiwGQyqj76KKmCe+jTrvMnP3cdAQAS4/pHXCeAa5WeNLNZynDXbCxQLGLCVFZyOhSUW7lahR27mFoAwHOwVuoclB7vcJ0ErnEpXrzwUMSELRTYZwFJUuePrnIdAQBizRjpD0wroHB/BcfMxgfFIiZMNqua4491HQMxkFu9RoUntjK1AIBnYa20q09a1eo6CeJg3kQmFnHCQxEj2cmT5I0f5zoGYqDj0itdRwCA2Lp6qesEiINxtVJzjesU2BvFIkZsYDkdCpKk/IYNym/YyNQCAPZirbS+Q9ra4zoJ4oBjZuOHYhErVjUnnuA6BGKi/buXuo4AALEx8n2WXzGtwG5zJ0iiWMQKxSJGTCajqnlHytQw14Pk79ypoaXLmFoAgMIN28u2Sb3csg1JVZ50xDgpwzvZWOHhiBnjeapeeIzrGIiJ9u9fLgUB5QJAqlkbnvzzfw+7ToK4mDtByvIuNnZ4SGLGFnxVn3Cc6xiIi6Eh9f3zZhnDzT8A0ssY6bbHpTzHimK3BZM5ZjaOKBYxY7Keak44TvI811EQE92/+o2CgQGmFgBSyVppICf9Y43rJIiLigzHzMYVD0kMZWpquIUbT7JWHVf8xHUKAHDmtw+xRxdPOmK8VMH3X2OJYhFDtlBgORSeYuje+5Xf8gRTCwCpYm14tOzKXa6TIE5YBhVfFIsYMtmsak450XUMxEz7Jd+VJMoFgFQY+VL3swfc5kC8eEaaP4llUHHFwxJT2bFjVXHobNcxECP+zl0avHsxG7kBpIIx0pKtUteQ6ySIk8PGSVVZ1ymwLxSLmLK+r7qzznAdAzHTcdmVCoaHmVoAKGvWSnmf42XxTMdMYhlUnFEsYsp4nmrPPJ3TofBUvq/OH/3YdQoAKLlrl0s+30PBXozC/RUsg4ovHpoYy9TXqfqY+a5jIGYG77lXuXWPM7UAUJaslTZ3hcuggL3NHivVVrpOgedCsYgx6/uqPZvlUHimtq9/S/J9ygWAsmJtOKW48j7XSRBHnAYVfxSLGDOep5oTj5epqXEdBTFjBwbU9bNfuo4BAKPuuuXSUMF1CsRNxkjHTWEZVNzx8MRdNquak05wnQIx1H/Lbcpv2MjUAkBZsFba1Ck98ITrJIijOeNZBpUEFIu4CwLVvuAs1ykQU20XfVMKAsoFgESzNlziwhIo7MsJU1kGlQQUi5gznqeqo+bKa2lxHQUxFPT1qesXv+ZuCwCJd81yadh3nQJxVJXlUryk4CFKAmtVc/qprlMgpvpvvFm5TZuZWgBIJGulDR3SUk6Bwj4cMym8cRvxR7FIAmNUdw7LobBvbf/zDU6JApA41kqFQLqKJVB4DidNC58riD+KRQIYY1Qx9RBVTJ/mOgpiKujtVccPf+Q6BgAcsKuXSjnWzmMfmmukQ8dKGd6xJgIPU0JY3w9v4gb2YXDxfRpcfK/rGACw35bvkB7d6ToF4uzEqVLAtCIxKBYJYTxPtS84U2KTLp5Dx/cuU6GjkyVRAGLNWqk/J/1qieskiDMj6eTp4Y9IBopFgnhjxqhq3lGuYyDmdn3xqxxBCyC2rJWspEvvCX8E9mX22HApFN9TTQ6KRYLYgq/as1gOhecWtLap86qfuY4BAPv0h0eknX2uUyDuTuLuisShWCSIyXqqOfVkmaoq11EQcwO33aGhpQ+5jgEAz/DIDmnxZtcpEHfVWWnhFO6uSBoeroQxlZWqPeM01zGQAO2XfFd+Ty9LogDEgrVS96D0S/ZVYD+cOFXK8i41cXjIksZa1b/8Ja5TIAmCQK1f/IoUWMoFAKeslXwrffcu9lVg/5w5y3UCHAyKRcKYTEYVh0xR1bwjXUdBAhS271TH934oSZQLAE6MfOm56j6pZ9htFiTD4WOlcXVs2k4iikUCWd9X/Utf7DoGEmLwvgfUe8NfZfgKDcABY6S/rZbWtbtOgqQ4YyabtpOKYpFAxvNUffyx8saPcx0FCdHz2//T0PJHXccAkEIrdkj/etx1CiTFmGpp3iQ2bScVD1tSWav68891nQIJ0nbRxSq0trEkCkAkrJXaB6SfPug6CZLk1OmuE6AYFIuEMp6nuvNeKFNZ6ToKEmTnpz8vm8tRLgCUlLVSIZC+s8h1EiSJZ6TTZkgZVu4mFsUiwUxNjWrP5OhZ7D/b16/WL10kWU6KAlAaI19aLrtHGsy7zYJkmT9JqueqrkSjWCQZR8/iIOQ3bFDn5VdJ4qQoAKNr5EvKdY9Im7vdZkHynDFLCnhZSjSKRYKFR88ewtGzOGADd96lnutvcB0DQBkZKRU3rZXu5WZtHKBJDdLsFpZBJR3FIuGs76v+JRe4joEE6v2/32vgzrtcxwBQJoyR7tks3fiY6yRIotNncMRsOaBYJJzxPFWfcJy8cWNdR0ECdV52pYaWP+I6BoAysHy79Ae+nOAg1FVKJ03jiNlywENYDqxV/QUcPYuD03bRN5XbuIn9FgAO2uPt0i+WuE6BpDpzJqWiXPAwloHw6NkXcfQsDtquz3xRfls75QLAAdveK12+2HUKJFWlJ501i70V5YJiUSZMTY1qzuDoWRykINCOT3xKQW8f5QLAfrFW6hqUvnOnxFcNHKyTp0tVWdcpMFooFuXCWjVw9CyKkctpx8c/KTswSLkA8Jyslfpy0sW3ST5fLnCQMkZ64aGuU2A0USzKhMlkVDH1EFUtOMZ1FCSY7evXjo99UnZoiHIB4FlZG158943bpByn+KAIx06RxlSHJ4qhPFAsyoj1fY15w2tcx0DCBT092vHxC2WHhykXAJ7CWmm4EJaKoYLrNEi6Fx3GhXjlhmJRRoznqfKwQ1W1YL7rKEi4oLNLOz52IZMLAHtYK+V86eLbpf686zRIuiMnhJfisWm7vFAsyoz1fTW+/rWuY6AMBJ2d2vHRT8oOsucCSLuRUvHN26WeYddpUA5edKgUsJSu7FAsyozxPFUdfqiqjjnadRSUgaC7W9s//F8K+vopF0BKjeypuOhfUteQ6zQoB9ObpNljpQzvQssOD2kZsr6vxjcwtcDosH192vGRTyjo7qZcACljrdQ7LF10q9Sfc50G5eJFh0k+04qyRLEoQ+HU4jBVzWdqgdFhBwa1/YMfV2HnTtdRAERoV19YKoZ810lQLqY0SPMncdN2ueJhLVPW99X4RqYWGEWFgnZ+7ELl1m90nQRABB5vl751h1RgUIlR9JK5TCvKGcWiTD05tZjnOgrKibXa9ZkvaOiRR10nAVACI6sdl22VLlvMjdoYXdPGSPMmMq0oZzy0ZYy9FiiVtq9drP47F8lay74LoEyMfCrf/rh09TK3WVCeXjqHaUW5o1iUMeN5qjricFUdzdQCo6/z0ivVc+0fJIlyASTcyKfwn1dKf17lNgvK08xmac4EphXljoe3zDG1QCn1/uFP6vjB5RKTCyCxrA2XPP16mXTHBtdpUK5ext6KVKBYlDnjeaqaw9QCpTN492Lt+uyXZHM5ygWQMNZKhUD6wV3Ssm2u06BcHTZWOnQs04o04CFOgXBq8RrXMVDG8hs2avsHPiq/vd11FAAHoG9Y+n+3SZu7XSdBOWNakR4UixQIpxZHqGreUa6joIzZvn7t+ODH95wYxfQCiKeRT821bdJXbpG6uU0bJTR3vDSjmWlFWvAwpwRTC0Sl7WsXq/cvf5dEuQDiZuRT8sbHpB/dy3GyKL2XzpUCphWpQbFICeN5qpo7R9XHLnQdBSnQffVv1fG9y6QgoFwAMWGt5FvpyvvCYgGU2tETpaljpAzvNlODhzpFrO9rzDvfKnme6yhIgcHF92rHxy5U0N3jOgoAhUuevnar9Fib6yRIg4yRXnkU04q0oVikiPE8ZSdOUP0F57qOgpTwd+3S9vd9WAMPPMhleoAD1oYfS7eGpaJ32HUipMVpM6SxtUwr0sZYXulTxVorOzSkHR/+hILePtdxkCK155yt5ve+S8pkZIxxHQcoe9ZKgZWueVhastV1GqRJTYX0hRdK1VmJL/fpQo9MGWOMTGWlGl/PRm5Ea+C2O7T9o/+toKvLdRQgFXqGpYv+RalA9M47XKqiVKQSxSKFjOep7rwXKTttqusoSJmgtU3b3/cRDdx7P0ujgBIYWfr08HbpqxwlCwfG1Ulnzgz3WCB9KBZpZa2a3vlW1ymQUh3f+YE6Lr1C8jk1Chgtey99+uUS12mQVq880nUCuESxSCnjeao+ep6qjz/WdRSk1OCiu7X9vR9SfsNGphdAEUY+dbb1SF+5WXrgCbd5kF6HjZWOnsRleGnG5u0Us0Egv7VNOz5+oeT7ruMgxerOe1E4QfM8NnYDB2BkSnHDSumuja7TIM2MpE+eJU2s5ySoNOOhTzGTycibMF71LznfdRSkXP/Nt2rbez6k/PoNTC+A/TDyKbJ995SCUgHXTpwmTW6kVKQdD3/KGWPU+PrXKNPQ4DoKUs7292vXZ7+krh//TPJ9ygWwDyOfG50DVt+5S+rPOw6E1Kv0pJfPDadnSDeKBWQqKtT4xte6jgFIkvpvuU3b3vMh5R5negHsbeTzIb9+g7p+/is11Uhnz3KdCpBedJhUW8lJUKBYQLuPn33ROaqYPs11FEBSOL1o/dyX1HHpFbK5XPhrFAyknM3l1PGDy7Xrs19S/z9vUn79Br1krtX4OtfJkGYT6qQXHkqpQIhigZC1anrX21ynAJ5icNHd2vb296j/9jskUS6QPiNTioG779G2d7xXg3cv3vO/tX39WzJBoDcssOI9HVx5/TGuEyBOKBaQFE4tqo46UtUnHu86CvBUQaDOy3+s7R/+hPKbNrM8CqmwZ9nTlie046OfVMf3L3/G6X1BX596f/M7zW4xOmW6o6BItRMOkWaP5XhZPInjZrGHDQL57R3a+YlP7Vl+AsRN9UknqOWD71WmtlbWWo6nRVkZeU4HAwPq+NGPNXTfA8/730z4zrekSZN18e1GXdy0jYjUVkifO0eqrmAZFJ5Ex8QeJpORN7ZFja9/jesowD4N3f+gtr3zfer9+41SwM3dKA97JnFBoN6//kPb3vm+/SoVktR20cXyjNXrjuFzAdF5+ZFSdZZSgaeiWOApTCaj+pe/RBWHznYdBdg3a9X9i6u19T8/oKGHlrM8Cok28twdemCJtr7zfer+1W+evKhiPwQdHer/4w06coLRcYeUKiXwpFnN0inTubMCz8RSKDyD9X0Vtu/Qzgs/x43cSARv4kS1fPQDqjzsUElieRQSYeTlN79+g9q/80P5ra1F/X4TL/ueCk1j9Y3bjPpYzYoSyRjpwrOl8bUUCzwTxQLPygaBev/wJ/Vcd73rKMB+y86YrpYPv3/P0ckUDMTRyMtu4Ymt6rj8KuUfXz8qv292yhSN/843tXy7dPUynvsojRceKr1srsSXVzwbigX2yfq+dn7q8ypsecJ1FOCAVBxxuFo+9D5lJ02URMFAPOwpFDt2qvNHP1Zu9ZpR/zPGvOvtanjxefrpA9KKnaP+2yPlmmukz7xAqvBcJ0FcUSywT9b3ld+8Rbs++yUpCFzHAQ5Y5ZFz1fzedyo7ZYokCgbc2HtC0fnjn5ekUOxt0o8v12B1g75xm9FQoaR/FFLmPSdJR4zjeFnsG8UCz8laq+5fX6O+v/zNdRTgoFUcfqha3vduZadNlUTBQDT27KHYsEkdV/5EhQ0bI/lzKw6brfEXfVX3bZGue4TnOkbHcVOktxznOgXijmKB52Xzee385GdV2L7DdRSgKN4hh6jlPe9Q5ZFz9/waJQOjae+X1Nxja9VxxU/lb90aeY7mj35AdWecrssXS+vaI//jUWYaq8IlUJUcL4vnQbHA87K+r9y6x9X6pa8d0BGIQFyZ+no1/+fbVXPyiTLZLBftoWgjzyHr+xp84EF1/eSXCnp63AXKZDTpp1eox9ToW3cY5TjgD0VgCRT2F8UC+63zp79Q/423uI4BjB5j1PBvL1f9y16sTGPj7l+iYGD/jbyEBt096vv7P9V7w19j8w2YqvnzNPYLn9Gd643+vMp1GiTViVOlNy10nQJJQbHAfrHWyuZy2vmJT8lvY66O8lN51JEa89Y3qXL2rPA7z0wxsA97phPWKrd2nbqv/q1ya9a6jvWsxn76v1V13EL94G6jzV2u0yBpmqqlT79AqvQ4Xhb7h2KB/WZ9X8MrVqntootdRwFKxjQ2qOk/3qCa005RprqagoE9Rp4LwfCwBhbdra5f/04aGHQd67lls5r0s6vUXqjQ/95h5POKjwPw/lOkQ1tYAoX9R7HAAeu47EoN3LHIdQyg5KpPOE4Nr3qlKg+dLZPJUDJSaM90IgiUW/u4em/4i4YeXOo61gGpOeUktXziI7pprdGNj7lOg6Q4dbr0umNcp0DSUCxwQKy1skND2vGxCxV0dbmOA0TD81T/0her7rwXKjtxAkulytzeS50KW7ep78Zb1H/zrYm+z2f8V76g7Nw5+s4io+29rtMg7lpqpU+fLWUzLIHCgaFY4IBZ39fQQ8vV/s1vu44CRM7U16nxNf+u2tNPU2ZMIyWjTOxdJvzOLg3ceZd6rr9BGhpyHW10VFdr8k+u0PYBT9+9y4gXfuyLkfSh06QZTSyBwoGjWOCgdf38V+r7x02uYwDOZMaOVcMrX6qaE46XN24sJSNhnlomOjW05CH1XH+DgvbyPKCi9oUvUPP7/lN/Xml0xwbXaRBXZ82S/n2e6xRIKooFDpotFLTr819RPqLbZIFYq6xU/fnnqvbM01QxfZqM5+05ipSiEQ97Px7W95Xf/IQG7l6svhtvloaHHaeLxoSLL5KZMUOX3GHUNuA6DeJmUoP0X2eGS6CAg0GxwEGzvi+/vUM7P/lZ2XJZLgCMkuoTjlP9Beepcs7hMlVVe74zLlE0orT3VMIODWl49WPqv+kWDS1Z5jqaE5nGBk284lJt7M7ossU8D/Gkioz032dJY2tZAoWDR7FAUazva3Dxfer4weWuowCxlRk7VnUvPEvVCxeqYvpUmcpKikYJPGUiYa1sPq/C1q0aWvqw+m66RUFnl9uAMVH/ypep6S1v0rXLpXs3u06DuHjdfOnk6VKGL0coAsUCo6LjR1dp4LY7XccAEiEzZozqXni2qo87VhUzpjHROEjPKBJDw8pv3qKhZQ+p/7Y7FXR2Ok4YXxO+d4nshEm6+HajbgbOqbdwsvS2412nQDmgWKBo1lqpUNDOT31eha3bXMcBEsfU1ar2tFNVfewCVcyYLq+5SfI8ysZenv73YH1ffkenco9v0NCyhzR43/2ycb+sLkYy48Zq4g+/qzVtRj95IN3PrbRrqZU+dZaU9ZhWoHgUC4wK6/sqbN+hXZ/5omwu5zoOkHiZhnpVn3iCqo6ep8pZ0+WNG7dnCZX05BttqbxKxzMKhLWywzn57e3Kb9ys4ZWrNPjgEpY1jYLGN71eja96pX61VHqI7wmlkmekj50hTW5gXwVGB8UCo8YGgfr/dYe6rvqp6yhAeTJGFbNnqXr+PFUcOlvZiRPkNTcpU1e3Z8IxIo7FY1+ZrLVSECgYHJTf3qHC1m3KrVuvoUcfVWHTFomXqZKZePkPlG9s1jduM+rPu06DqL3iSOns2UwqMHooFhh17d/9oQYX3+c6BpAunqfstKmqnDlD2UMOUXbSBHljW+Q1jVGmtlamsvIZ5ePZHOhLwn79fn4gmxtW0Ncvv6dHfmeX/NY2FbZtV37TZuXWb5DyvKt1ITv1EI3/34v10HbpN8t4d5kmc8dL7z3ZdQqUG4oFRpUNAtlcXjsv/Kz8nbtcxwHwdJmMTH29vObmcNoxplHemEZlGhvlNdTLVFVK2QqZjJGUkTKSTCa8jnfPq4WVrJUdGFIwMKCgr09Bb5/8nh4Fvb3yu7oVdHUp6OmVgsDZ/1Xsn6b3vFP1571IP75fWsWX7VRorJI+dbZUXcG0AqOLYoFRF148tUW7PvdlyfddxwEAPI9JP/mRBirr9Y3bjYYLrtOglIykD54qzWxmXwVGH08pjDrjeaqYMV1j3vxG11EAAPuh41vfVn2l9Iq5fK+x3L14jjS7hVKB0uBphZIwmYwaXv4SVR9/rOsoAIDnkXtsnYbuvU+nzTQ6tMV1GpTKMZOk8w6XYnKeA8oQxQIlY4NALR/+gLyxvEoBQNx1/OByFQYG9caFVhW8Oyg7E+ul/zhWChhKoYT40oGSMZmMTHWVxn7mkzJVVa7jAACeSxCo87s/VHONdMEc12EwmmoqpHefFN5bwWZtlBLFAiVlPE8V06aq+cPvZ/YKADE3/PBy5R5arhfMtpo6xnUajAYj6a3HSk3V7KtA6fEUQ8mZTEY1J52gxte92nUUAMDzaPv29xXk8vqPhVYe3w9KvBfPkeaMp1QgGjzNEAljjBpf+yrVnH6q6ygAgOeSy6n78qs0sV4651DXYVCM+WzWRsQoFoiMtVYtH3qfKg6d7ToKAOA5DC6+V7k1a3XBEVYT612nwcGYWC+9mc3aiBjFApExxkjGaNxnPymvhZOiACDOWi++RPJ9vWmhFd/wTpbqrPTuE9msjehRLBAp43nK1NVp7Gf+m5OiACDOBgbV84tfadoY6YxZrsNgfxlJbz1Oaq5hXwWix1MOkQtPipqm5g+9j4WfABBj/Tf/S/lNm/XyuVYtNa7TYH+8ap40d7yU4R0eHOBpByeMl1HtKSep8bWvch0FAPAc2i76pjLW6g0LWKwfd2fNCqdLfM8OrlAs4FTj616tmtNOcR0DALAPQU+Peq+9ToePMzppmus02Jf5k6R/O8p1CqQdxQJOcVIUAMRf35/+ovz2HXrVPKtGtsfFzvQm6S3HSsyU4BrFAk4ZY6RMRuM+80llmptdxwEA7EPr17+prLF67XzevsZJS630npM4AQrxQLGAc8bzlKmv07jPflKmstJ1HADAswh2tWrgb//Q0ZOMFkx2nQaSVFMhve+k8HhZNmsjDngaIhaM56li+jQ1f+QD7DoDgJjq/vXvlG/v1GvnW9VWuE6Tbl5GetcJ4cSCY2URFzwVERsmk1HNSSeo6T/f4ToKAGAf2r/xLVVnpX+fx5Iol954jDSrhVKBeOHpiFgxxqj+/Bep8fWvcR0FAPAsCpu3aPC2O3TCVKO5412nSacLjpCOn8qeCsQPxQKx1PjaV6n+Jee7jgEAeBZdV/5Ehd4+vWGBVZXnOk26nDEzLBZAHFEsEFtN73ybas44zXUMAMCz6PjWd9RQKb3sSJZEReWEQ6RXH+06BbBvFAvE1sgdF9ULj3EdBQDwNLk1j2no/gd0xkyjWS2u05S/oydKb1woWXocYoxigdgyxkjGaOyFn1DlEYe7jgMAeJqO712qwsCg3rTAKss7ipI5fJz09uPDf+bgRMQZXwYQayaTkTxP4z7/KVXMmO46DgBgb0Ggzu9fppZa1v2Xyowm6d0nhoWCzdqIO4oFYs9kMjKVlRr/5c8pe8gU13EAAHsZXvaQcssf1TmHWh3S6DpNeZnSIL3vlPBIWUoFkoBigUQwnidTU6PxX/68vIkTXMcBAOyl7ZLvyuYK+o+FljfAo2RcnfSBU6UKSgUShGKBxDCep0xDvcZ/5QvyxrJTEABiI5dT1xVXaVKDdM5s12GSr6la+tCpUnWWC/CQLDxdkSjG8+Q1jdH4L39emTHM3AEgLgbvXqzc2nW6YI7VhDrXaZKrvlL64Knhj5QKJA1PWSSO8Tx548eF5aK+3nUcAMBurf/vEhk/0JsWWrF658A1VEkfPk1qrqFUIJl42iKRjOcpO3mSxn3xMzJ1ta7jAAAkaWBAPb+6WjOajU6f6TpMsjRWSR85TRpbS6lAcvHURWIZz1PF9Gma8NUvKtPIsigAiIP+G29RbvMWveJIq+Ya12mSYUx1WCqYVCDpePoi0YznKXvIFI3/2peUaW52HQcAIKnt699Sxlq9/hiuiX4+TbtLRROlAmWApzASz3ieshPGa8LXvyJvwnjXcQAg9YLOTvX94XrNGW90wlTXaeKruUb66OnhxIJSgXLA0xhlwXievOYmTbjoK1yiBwAx0PuHPym/c5dePc+qocp1mvhpqQ1LRUMVpQLlg6cyysbIPRcTvvZlVcyc4ToOAKRe29e/qYqM9OqjWRK1t3G10kdP40hZlB+ezigr4Q3d1Rr/1S+o8vDDXMcBgFTzd+zUwD//qQWTjeZPcp0mHsbXSR85XaqjVKAM8ZRG2TGeJ1NVpfFf/pyq5h3lOg4ApFr3L3+jfGeXXjffqqbCdRq3JjWEG7VrKygVKE88rVGWTCYjZbMa9/lPqfrYha7jAECqtX/jEtVUSP92ZHqXRM1qCZc/1VAqUMZ4aqNsmUxGymQ09lOfUM2pJ7uOAwCpVdi4SYN3LtJJ042OGOc6TfTmTZQ+cIpU6VEqUN54eqOsjZSLlo99SLUvOMt1HABIra7Lr1Khr19vXGBV6blOE52Tp0nvPEHyjJThXRfKHE9xlD1jjGSMWj74XtVdcJ7rOACQWh2XfFeNVdJL57pOEo0XHSa9YYFkJBnjOg1QehQLpILZ/RW9+T/froZ/f4XjNACQTrlVqzX84BKdOdNqZrPrNKVjJL1qnvSy3QWKUoG0MNba9O6kQmr13XSrun7+K8n3XUcBgHTJZDTp51ep06/SJXcaFQLXgUaXl5H+Y6G0cDKFAunDxAKpVHfuORr3uU/J1Na6jgIA6RIE6vrB5RpbJ51bZtcNVXnSe0+SFlAqkFIUC6SSyWRUddRcTfjG/8ibOMF1HABIlaElS5V7dKXOPdxqSoPrNKOjvlL68GnSoWOlDKUCKcVSKKSa9X3ZoSG1Xfxt5dY85joOAKRHZaUm/exK7RzM6rt3GQUJfjcypUF6z8lhueA4WaQZT3+kmvE8mepqjf/y51R71hmu4wBAeuRy6v7xTzWlUTp7tuswB2/eROljZ1AqAIliAch4nuR5avnw+9X4hteyMBYAIjJ4x13KP75eL5ljNb7OdZoDd86h0rtOkLIZSgUgsRQKeAprrQbve0Cdl14hm8u5jgMAZS9TX6+JV16mzb0ZXXqPURLelHgZ6XXzpZOmuU4CxAv9GtiLMUY1J52g8V/7kjJNTa7jAEDZC/r61Pub32pWi9GpM1yneX51ldIHT5FOmOo6CRA/FAvgaUwmo4rp0zTxmxepYsZ013EAoOz1/f1G5Z7YqlceZdVU7TrNvk1qkP77TGl6Eyc/Ac+GYgE8C+N5yjQ2aMLXv6Lq449zHQcAyl7bRd+UJ6vXHxPPxVBHTpA+frrUUMV+CmBf+NQA9sF4npTNauynPqH6l7/EdRwAKGtBR4f6/niD5k4wOv4Q12me6uxZ0n+eKGU9SgXwXNi8Deyn/n/doc6f/kLK511HAYCyNfHS76rQPE7fuM2oz/EZGlWe9MYF0oIpbnMASUGxAPaTDQIVtm1X+yXfU2H7dtdxAKAsZSdP1vjvfkvLd0hXL3W3kWFifTilaKllPwWwvxjoAfvJZDLKTp6kCZd8XTWnn+o6DgCUpcL27Rq48WYdO8Vo3kQ3GY47RPqvM6XmGkoFcCCYWAAHyForY4z6brlNXT//FUujAKAEJl11mQZrGvWN24yGCtH8mV5G+vejpNNnStZyXypwoJhYAAfI7H6lqXvh2Zr4zYuUnTzZcSIAKD/tF39bdRXSK4+K5vufzTXSx07Xnrs0KBXAgaNYAAeJpVEAUDr59es1eNc9OmW60eFjS/tnzR0vffIsaXIDS5+AYrAUCigSS6MAoEQyGU36yRXqydToW3cY5fzR/e2NpPOPkM4/XLKiVADFYmIBFOkpS6Mu/hpLowBgtASBOr79PTXVSC+eM7q/9Zhq6f2nhKXCGEoFMBooFsAoMZmMslMma8K3LmJpFACMktyKlRpeskxnz7Ka0TQ6v+cxk6RPnS3NbmEvBTCaWAoFjLInl0b9S10/v5qlUQBQrGxWk352pToKlbrkTiM/OLjfptKTXjVPOnk6pz4BpcDEAhhlTy6NegFLowBgNBQK6vrh5RpfJ5172MH9FtObwinFidPCn1MqgNFHsQBKZGRp1MRLvq6688/lVQwAijD0wBLlVq7WuYdZTW7Y//8uY6TzDpc+errUVM1eCqCUWAoFlNjI0qjhlavVcfmV8ne1uo4EAMlUXa1JP7lCOwY9fe8uo+B53sG01EhvOU6a0cT3doAoUCyAiFjfl3xfXb/+nfpvvCVc4AsAOCC155yl5ve/R39eZXTH+n3/e8cfIr1ufnibtsf6DCASFAsgQnumF6vXqOOyq+Tv3Ok6EgAkzoSLvyYzY6YuucOobeCp/1tdpfSao6WFU9igDUSNYgE4YH1fCgJ1//oa9f3zJqYXAHAAMo0NmnjFpdrYndFli59sDgsnS6+dL1VlmVIALlAsAMeGH1urzsuuVGH7DtdRACAx6l/+UjW97T907XJpxc6wUMyfxJQCcIliATi2Z3rx22vV9/d/Mr0AgP004bvfkiZOlm+NKj2mFIBrfAoCjhnPk6mo0Ji3/YfGX/Rl7r0AgP3gjR+voKtbmYxRNUufgFhgYgHEiPV9yVp1/+469f3170wvAODpMhnVv+zFGvPG10mZjIznuU4EYDeKBRBD1lrl129Qx6VXqLB1m+s4ABALFbNmqvkD71HFjOkybKQAYodiAcSU9X1JUt9f/6Ge62+QHRx0nAgA3DBVVWp83atV//KXSNYypQBiimIBxJwNAgV9/eq++rcauPMulkcBSJXqYxeq6T3vlNfSLJNhIwUQZxQLIAFsEMhkMso9vkFdP/2Fcusedx0JAEoqO22qmt7xFlXPP3rP10AA8UaxABLE+r6M56n/9kXq/u3/Kejqch0JAEZVpqFBjW94jerOfSHLnoCEoVgACWR9X7ZQUM+114d3X+zejwEAieV5qn/x+Wp8/atlKispFEACUSyABLPWyt/Vqq6f/UpDyx5yHQcADkr1Ccep6R1vkTd+vCRx4hOQUBQLIOFG1h4PPrRc3T+/WoXt211HAoD9UjF9msa8822qnnck+yiAMkCxAMrEnuNp//ZP9fzhTxxPCyC2Mo2Nanzja1X3whewjwIoIxQLoMzsOZ7217/TwB2LOJ4WQHxks6p/yQVqfN2rZSqyFAqgzFAsgDJkrZUxRvmt29RzzXUavP9BCgYAp6pPPCHcRzFuLHsogDJFsQDK2Mia5fzmLeq+5joNPbjUdSQAKVM5d47GvOn1qjpyDvsogDJHsQBSYOT+i9zGTeq55joNLX3IdSQAZa7qqCPV+PrXqOqouXu+BgEobxQLIEX2FIz1G9T9u+s0/PBy15EAlJmqeUep8Q2vUdXcORQKIGUoFkAKjbzYD699XD3XXKvhR1a4jgQg4armHx1OKOYcTqEAUopiAaTYnoKx5jH1/O46Da9c5ToSgISpWjBfja9/raoOP5RCAaQcxQLAkwVj5Sp1/+465dY85joSgJirPnaBGl//GlUeOptCAUASxQLAXkbeHAw9skI911yn3Np1riMBiJnq448NC8WsmRQKAE9BsQDwDHsvker9y9819MAS7sEAUq76hOPCQjFzBoUCwLOiWADYp5E3D4X2dvX99Z/q/9ftsoODrmMBiEpFhWpPO0UNr3ipKqZPo1AAeE4UCwDPy1orWSubz6v/X7er/x83qbBjp+tYAErEG9uiuvPPVf15L1Kmvo6L7QDsF4oFgANifV/KZDS07GH1/fUfGn6Uo2qBclF11JGqe8n5qjnxeMlaphMADgjFAsBBGVkSkX9iq3r/+g8NLLpbyuddxwJwgExVlWrPPF31L7tAFYccwnInAAeNYgGgKDYIJGNkBwbU98+b1XfTLQo6u1zHAvA8vIkTVH/+uao79xyZ6upwQsFyJwBFoFgAGDXW9yVJA/fcq76/36j84+sdJwLwFMao6pijVf/SC1S9cIEUBEwnAIwaigWAUTeylCL3+Ab133qbBu5ezGlSgEOmpkZ1LzhT9S+9QNmJE1nuBKAkKBYASmZkmZQKBQ3e94D6/3WHhles5E4MIArGqHLuHNWedbpqzzhNpqJCMkbGGNfJAJQpigWASNiCL5P1VGjv0MC/blf/7Yvkt7a6jgWUney0qao983TVnX2GvOZmphMAIkOxABApa+2edd3DK1ep/45FGrz3AZZKAUXwWlpUc/qpqnvBmaqYNpUyAcAJigUAZ0buxJDva/DBpRq48y4NLXtY2r0JHMC+mZoa1ZxyourOPlOVR84Nlxiy1AmAQxQLALEwslQqGBjQwN2LNXDn3co9tpb9GMDesllVH7tAtWeerpoTjpM8j5OdAMQGxQJA7Oy9H2Pw7sUafHBpWDKCwHU0IHrGqHLOEao98zTVnn6qMrW1LHUCEEsUCwCxNvIGKujv1+CDSzX04FINPfyI7NCQ62hA6WSzqjpyrqqPW6jaU06SN7aFMgEg9igWABJjZJJhCwUNr1ilwQeWaGjJUvntHa6jAUXLNDWp+tgFqjn+WFUtmK9MVZVsoSCTzbqOBgD7hWIBIJHs7mVRJpNRbtNmDd3/oAYfXKr8ho1ugwH7yxhVHjpb1cctVPWJx6tyxvSnnJoGAElDsQCQeHu/GfO7ujV4/wPhkqkVq6R83nU8YA9TU6PqBfPDMnH8cfIa6vecjsZpTgCSjmIBoOyMLB+xuZwGlz2s4Yce1vDKNSps3+46GlIoO2Wyqo8/VjXHH6fKOYfLeN6eZX0AUE4oFgDK2t7fDfZ7+zS8YqWGV65SbtUa5Tdv4ThbjDpv4kRVzT1ClXMOV/WCY5QdP+4pS/cAoFxRLACkivX98BKxTEbB4KCGV67W8MrVyq1erdz6jVzOhwPjeaqYOUNVc+eocu4RqjpqrryGBkli4zWA1KFYAEg1GwSSteHylFxOw4+t0/CKlcqtWqPhtevYo4GnMLW1qjriMFXOOUJVRx2pysNmy1RUPOV5BABpRbEAgL08pWj4vnKPbwiLxurHlNu4UUFnl+uIiJA3fvyeZU1V845SdspkGWNkC77kseEaAPZGsQCA5/D04z+D/n7lN21WbsNG5TdtUX7TZuWf2MpkI+kyGWUnTVTFtKmqmD5N2enTVDX3CHljxkhiWRMA7A+KBQAcIGttuBfD88LvXgeBCjt3Kb9ho/IbN4WFY/NmLu6LI2PkjRuniulTwxIxbaoqZs1UdtLEPcXBFgrhhn82WgPAAaFYAMAosbs3fu+ZbgwMhtONjRuV37hZ+c1bVNi2XXZw0GXM1Mg0N6ti2iGqmDYtLBIzZ6jikCkylZWSnvl4AQCKQ7EAgBJ6+nRDkoLBQfmtbSrs3KVCa5v81jb5bW17/jno7XWcOiE8T15Li7ITxskbN07e+HHKjh+n7JQpqpg+VZmaGkm7980EAUuZAKDEKBYA4MjIG96nL7uxuZwK7R3yd+5SobU1LCG7S0ehtU1BV1f537+RzcprbpLX3Bz+2NKsTHOzsmNb5E2cqOyEcco0Nj71761QCI8SZgIBAE5QLAAgpvZMO572Ztn6voKBAdmBQQV9/Qr6esMf+/sV9A/IDgwo6B8Ifz4wIDvyz7t/jOSujkxGpqZamZoamZqavX6sDn+srZGprt79Y/jzTEO9vLFj5TU1KVNX+9S/i5ESRnEAgNiiWABAGbC+/+QUw5g9t40/67+bzysYHJQdHHryDbv05I/WhqXmWT6e8uuBlWQlkwkLQ22tTHWVMtXVMhUVz5M3kGzw5C9QGAAg8SgWAAAAAIrGWXoAAAAAikaxAAAAAFA0igUAAACAolEsAAAAABSNYgEAAACgaBQLAAAAAEWjWAAAAAAoGsUCAAAAQNEoFgAAAACKRrEAAAAAUDSKBQAAAICiUSwAAAAAFI1iAQAAAKBoFAsAAAAARaNYAAAAACgaxQIAAABA0SgWAAAAAIpGsQAAAABQNIoFAAAAgKJRLAAAAAAUjWIBAAAAoGgUCwAAAABFo1gAAAAAKBrFAgAAAEDRKBYAAAAAikaxAAAAAFA0igUAAACAolEsAAAAABSNYgEAAACgaBQLAAAAAEWjWAAAAAAoGsUCAAAAQNEoFgAAAACKRrEAAAAAUDSKBQAAAICiUSwAAAAAFI1iAQAAAKBoFAsAAAAARaNYAAAAACgaxQIAAABA0SgWAAAAAIpGsQAAAABQNIoFAAAAgKJRLAAAAAAUjWIBAAAAoGgUCwAAAABFo1gAAAAAKBrFAgAAAEDRKBYAAAAAikaxAAAAAFA0igUAAACAolEsAAAAABSNYgEAAACgaBQLAAAAAEWjWAAAAAAoGsUCAAAAQNEoFgAAAACKRrEAAAAAUDSKBQAAAICiUSwAAAAAFI1iAQAAAKBoFAsAAAAARaNYAAAAACgaxQIAAABA0SgWAAAAAIpGsQAAAABQNIoFAAAAgKJRLAAAAAAUjWIBAAAAoGgUCwAAAABFo1gAAAAAKBrFAgAAAEDRKBYAAAAAikaxAAAAAFA0igUAAACAolEsAAAAABSNYgEAAACgaBQLAAAAAEWjWAAAAAAoGsUCAAAAQNEoFgAAAACKRrEAAAAAUDSKBQAAAICiUSwAAAAAFI1iAQAAAKBoFAsAAAAARfv/i1AruNccbyYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n = 120 unique respondents\n"
          ]
        }
      ],
      "source": [
        "# Aggregate responses for \"Do you know what an electric pressure cooker is?\"\n",
        "epc_knowledge_counts = df_temp['Do you know what an electric pressure cooker is?'].value_counts()\n",
        "\n",
        "# Calculate percentages for EPC knowledge\n",
        "epc_knowledge_percentages = epc_knowledge_counts / epc_knowledge_counts.sum() * 100\n",
        "\n",
        "# Create an array with labels and percentages\n",
        "epc_knowledge_array = [\n",
        "    {\"Response\": label, \"Percentage\": round(percentage, 2)}\n",
        "    for label, percentage in zip(epc_knowledge_percentages.index, epc_knowledge_percentages)\n",
        "]\n",
        "\n",
        "# Display the array in the console\n",
        "print(epc_knowledge_array)\n",
        "\n",
        "# Plot the hollow pie chart without labels\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "ax.pie(\n",
        "    epc_knowledge_percentages,\n",
        "    labels=None,  # No labels on the graph\n",
        "    startangle=90,\n",
        "    wedgeprops={'width': 0.4, 'edgecolor': 'w'},\n",
        "    colors=['#e3555b', '#71b3ff']  # Professional color palette\n",
        ")\n",
        "\n",
        "# Add a circle in the center to make it a hollow pie\n",
        "center_circle = plt.Circle((0, 0), 0.6, color='white')\n",
        "ax.add_artist(center_circle)\n",
        "\n",
        "# Set aspect ratio to equal to ensure it's a circle\n",
        "ax.set_aspect('equal')\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig(fig_path + \"EPC Awareness.png\", dpi=500, bbox_inches='tight')\n",
        "plt.show()\n",
        "\n",
        "n_respondents = df_temp['Do you know what an electric pressure cooker is?'].notna().sum()\n",
        "print(f\"n = {n_respondents} unique respondents\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NNpkxL5ln75E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "outputId": "caa51240-b1df-4abb-d37c-f26c7e84cd85"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Filter rows where Connection type is 2, 5, or 6\n",
        "filtered_df = df_temp[df_temp['Connection type'].isin([2, 5, 6])]\n",
        "\n",
        "# Aggregate responses for \"Do you know what an electric pressure cooker is?\" by Connection type\n",
        "knowledge_counts = (\n",
        "    filtered_df.groupby('Connection type')['Do you know what an electric pressure cooker is?']\n",
        "    .value_counts(normalize=True)\n",
        "    .mul(100)  # Convert to percentages\n",
        "    .unstack()  # Reshape for plotting\n",
        ")\n",
        "\n",
        "# Rename Connection types for better readability\n",
        "knowledge_counts.index = knowledge_counts.index.map({\n",
        "    2: 'Individual metered',\n",
        "    5: 'Collective metered',\n",
        "    6: 'Collective unmetered'\n",
        "})\n",
        "\n",
        "# Plot a clustered bar chart for disaggregated EPC knowledge responses\n",
        "knowledge_counts.plot(\n",
        "    kind='bar',\n",
        "    figsize=(12, 8),\n",
        "    width=0.8,\n",
        "    color=['#4E79A7', '#F28E2B', '#E15759', '#76B7B2', '#59A14F', '#EDC949', '#B07AA1'][:knowledge_counts.shape[1]],\n",
        "    edgecolor='grey'\n",
        ")\n",
        "\n",
        "# Customize the chart\n",
        "plt.title('Do You Know What an Electric Pressure Cooker Is? (Disaggregated by Connection Type)', fontsize=16)\n",
        "plt.xlabel('Connection Type', fontsize=14)\n",
        "plt.ylabel('Percentage of Responses (%)', fontsize=14)\n",
        "plt.xticks(rotation=0, fontsize=12)\n",
        "plt.yticks(fontsize=12)\n",
        "plt.legend(title='Response', fontsize=12, title_fontsize=14, loc='upper right', frameon=False)\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Wmw7TTCK-aM"
      },
      "source": [
        "## Benefits and challenges of EPC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_WeZ73wHi3Sr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "outputId": "021666f0-eb43-4fbd-b334-06b4b3346a14"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x800 with 2 Axes>"
            ],
            "image/png": 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O3P727Vth2bJl4oOLyhIReU385bfPLetrmJiYCKVLlxb2798v3i+4c+eO0LBhQzGpFhsbq1C/ffv2AgBBR0dH2Lhxo/Dp0ydBEAThyZMnQu/evQUtLS0xGaesnzhgwAABgFCxYkVh27Zt4nX7+PGjcODAAaFixYoCAKFbt25y9c6ePSsAGQ+pLliwQHj79q24Lzo6WvD39xfc3d2FZ8+eKRwzK4cOHRKvddeuXYWHDx8KgpBxf2DLli1iIrdx48ZKk0FZ9YlzIj+Jv0ePHomJx/Hjx8ude2xsrHD27Fnh559/Fq5cuZKj9gThf/ekjI2NBTU1NWHatGnCmzdvBEEQhLi4OGHmzJni9VKWHM78u9mvXz8hKipKEISM93fdunWCVCoV+/J5SfytXbtW8PT0FK5cuSLec0lPTxcePnwojBkzRpBIJIK6urpw9erVAj+37HyNxN+kSZPEMuHh4YIgCMKFCxfEbStXrsz1cYkKEhN/REQqZP4ffeanxSwsLMTRV4aGhkL//v2FR48eKW1j8ODB4pcsVZYsWSIAEGrWrCm3PXMHw87OTkhMTFSom3mk1dOnT+X2bdmyRayrrJMgCIJw5coVQSKRCFpaWgojqr5G4k9HR0cAIISEhKgsk1v//POPeGxlo7xSUlLExGD16tUV9hd24s/e3l4AMkaw5UZO3gsLCwulI+euX78uljl37pzcvr///ltMlJ45c0ah7sePHwU7O7t8Jf6yEhUVJWhrawsSiUR4/Pixwv78nFtOyK6rVCqV+zug7CV7yk9ZfKoSfwsXLhTLLF26VNwuGyWceRQgEREREVFRM336dPH7bG4TPZllvlGvKvEyfvx4AYBga2ub6/Z3794tABBsbGwU9uU18ZffPresr6GtrS0mDjJ7/fq1IJVKBQDC1q1b5fbJkm8AhL///luhblpamtCyZUuxzOf9xDNnzghAxkPMqkbEPX36VNDT0xMACNeuXRO3L1iwQAAgtG3bVmm9vJL1hZs1a6Y0sZf5/sfu3bsV9hdm4m/nzp0CAKFy5cq5OmZOEn9Z9SddXV0FAELr1q3ltqenp4sz4rRp00Z8yFrZsfOa+MvOyJEjBUD5SOD8nFtOZD43c3PzLPvxyh4yzi7xFxcXJ86KZGpqKj5kvGHDBrGesvsDRF8T1/gjIsqBV69eia83b94gLS0NQMbi0XFxcXj16pVCnU+fPmH79u0AgClTpqhs+4cffgAA/Pfff0rbAYAJEyZAR0dHYXuHDh2gpaUFALhx44bcvo0bNwIARowYoXLthLp166JatWpITk5GYGCgyhi/FGNjYwDAixcvCqzNnTt3AgAaNWqEtm3bKuzX0NCAp6cnAODmzZsK162wfYlrIjN06FBYWloqbHdwcECFChUAANevX5fbt3v3bgAZ68c1a9ZMoa5UKsWkSZMKPFaZ0qVLo2bNmhAEASEhISrL5eXccuPTp09yfweUvbJa/yKztLQ03L9/H97e3pg+fTqAjHVO3N3dxTJv374VtxMRERERFVWy761AwX13lX1H/lzXrl0BAPfv30diYmKu2uzUqRMA4MGDB3j58mX+Avx/BdXndnNzg52dncJ2CwsLNGrUCIDqflr58uXRv39/hbpqamoqr2Pm2Pv374+yZcsqLVOmTBm0bNkSQMbadTKyPmvmeyP5df36dYSHhwPIeP/V1dUVynTu3BmOjo4AMtZOK0pk1+T9+/dISEgo0La1tbUxceJEpftkvxOffz7CwsJw//59AMCvv/6qdL14d3d3WFtbF2ismcl+586dO6eyTF7OLbeio6Oz7Md/vu5iVmJjY3Hq1Cm0atUKz58/BwCMGTMGamoZKZYv8feQKK+Y+CMiygEhY4S0+Pr48SOuXbsGd3d3HD58GM2bN8f+/fvl6oSGhooLEbdt2xYlSpRQ+sq80O/jx4+VHr9BgwZKt2toaMDCwgIA5L6spKWliQspe3l5qTx2iRIlEBERkeWxvyQXFxcAGV84J0yYgODg4Fx34D535coVAEDr1q1VlmnZsqXYkZCVLypk12Tq1KkYOnQojh8/jvj4+AJpW9XnCABKlSoFAApfeq9evQoAaNGihcq6eVkIPLP09HRs374dXbp0gbW1NXR0dOQWY//3338BQFwgW5m8nFtuuLu7K/wd+PyV1XXw9vYWz0dDQwOVKlWCl5cXkpKSYGFhgQMHDsDExCTP8RERERERFQempqawtbVVuk/2vR4A3r17p7D//fv3WLhwIVq0aAFLS0toaWmJ38F1dXXFcln1K3KqIPvc+emnNW/eXGlSBwCaNGkCDQ0NpfvOnz8PICMBmFXsAQEBCrE7OztDKpXi2rVraNasGTZu3IjIyEiV55ATsn65hoZGln3PNm3ayJUvKhwdHWFubo4XL16gQYMGWLlyJe7cuQNBEPLddrVq1aCvr690X3afD01NTTRu3FhpXYlEkuW1zomHDx9i4sSJqFu3LoyNjaGuri7+znXs2BFA1r9veTm33IqMjMyyHx8UFJRl/cz3JkxMTNC6dWuEhoYCAAYMGIDffvstX/ERfSnK//oTEVGWpFIpatWqhQ0bNiAmJgZ+fn7w8PDAkydPYGhoCADi0z8AVI7k+5yqpJeBgYHKOrIv8ikpKeK2mJgYJCUlAVDeIcrNsb8kHx8f3L9/H4GBgViyZAmWLFkCdXV11KpVC506dcLQoUNRunTpXLX5+vVrAMiynlQqhbm5OV69eiWWLyomTZqE//77D7t27cL69euxfv16SCQSVKtWDe3bt8ePP/6IKlWq5Knt3H6OgIynOAH5TvbncvseZZaYmAgXFxe5p1+1tLRgamoKTU1NABmf55SUlCyfnMzLuX1Nenp6YodGTU0N+vr6qFixIpydnTF48GCYmZnJlTczM0NUVFS+OzlERERERF9S5u+xMTExWfYbciIn3+sBxe/2d+/ehbOzs1ySQVdXF8bGxuJoHFm/vCBGZBVkn/tL9dO0tbVhbm6udISj7H5FfHx8jh40zRy7jY0NNmzYgOHDh+PChQu4cOECgIwRii1btkS/fv3QpUsXlQlJZWT9cnNzc2hra6ssV6ZMGbnyRYWxsTF27NiBfv364datWxg1ahQAwMjICM2bN0evXr3Qu3dvsY+bGzn5fKSmpsptl30+zMzMxFmilMlPX97Pzw99+/YVfw8AwNDQEFKpFBKJBMnJyXj37l2++/Gfn9vXZmVlJf4s+52qXbs2+vfvL46Ilfn87yFRYeKIPyKifPrpp58AAHFxcTh69Ki4PfOUFx8/fsx2tFB2I4ZyI/Oxjx07lqNje3l5Fcixc8PY2BinT5/G2bNnMXnyZPGJxNDQUPz++++oVKlSkZvC40vT1NTEzp07ERYWhpkzZ6JVq1bQ1dXFzZs3sWjRIlSrVg2LFy/+6nHlptOWG3PmzEFgYCB0dHSwdOlSPH78GJ8+fcLbt2/x8uVLvHz5UnwCtiCeliwsEydOFM/n+fPnuHv3Lo4fP45JkyYpJP0AiCOBw8LCvnKkREREREQ5l3kGm2vXrhVaHIMGDUJUVBTKly+P3bt34+3bt0hISMDr16/x8uVLPHv2TCxbEP2KotLnzms/TRb/6tWrcxS7r6+vXP3+/fvj8ePHWLNmDXr37o2yZcvizZs32LVrF7p164YWLVoU2Mw134rWrVsjMjISW7Zsgbu7OypVqoS4uDgcOnQIAwcORO3ateU+h1/Dl+rHv337Fh4eHkhKSkKrVq0QFBQktxTOy5cvxelov3WyfvzLly/x+PFjhIaGYsOGDQpJP6Do/D0kApj4IyLKt3Llyok/Z57eokSJEuLPX3saTTMzM/HpqMKYwjO3mjZtigULFuDcuXOIjY3FgQMH4ODggI8fP2Lw4ME5HjEJQFznLavpJGSJpczlC0rmp1BlU70qExcXl2U7NWvWhLe3N06dOoXY2FgEBASgefPmSEtLE0cFfg2yqWQzj2D9XH46L//88w8AYObMmRg7diysra0VOicFtQbHt8TZ2RlAxpOaWa2JQERERERUmFq2bCmOqPPz8yuUGJ4+fSquB75jxw64ubkprK9V0H2Kwu5z56SflpSUhOjoaKX7ZPcr8hO7qakphg0bhn/++QdPnjzB/fv3MXXqVEgkEpw9ezZXiU5Zvzw6OlpuBNnnZP38gu7HA//ry+enH6+np4eBAwfC19cXd+/eRVRUFBYsWACpVCo3EvBLk30+oqOjs1yLPq99+aNHjyI+Ph4mJiY4dOgQWrRoAR0dHbky32M/vl69euJ6n4X195BIhok/IqJ8ypxg0tPTE3+uX7++OKXCoUOHvmpMmpqa4qLXX/vYMrLkTW6fppRKpejSpQv27dsHIONLd24SH/Xq1QMAnDp1SmWZoKAgcbqI+vXr5yq+7GRep+3p06dKy9y9exexsbE5blNDQwPOzs44cuQItLW1IQiCuNbCl1anTh0AyHLe++zmxM+K7BrVrl1b6f5Hjx6Ji5J/TwYNGiSuQ+Ll5ZXj36P09PQvGRYRERERkRwrKyv06NEDALB9+3bcvXs3x3ULakaPzP0uVf2Kgu4/FXafW9ZPCw4OVlnm/PnzKqdJbNKkCQDg8OHDBRaTjY0N5s2bh379+gEATp48meO6sn58ampqluckex8Luh8P/K8vr6ofDwCXLl3KVZulS5fG5MmTMWHCBAC5uyb5Ift8pKSkiEnxzwmCgDNnzuSpfdk1qlKlitz6mZl9rXsWRYmGhgaGDh0KIOOeVG6uL/vyVNCY+CMiyqft27eLP8u+rAIZSUDZF94FCxbgyZMnWbZT0PN/y75sHD16VG4K0q9xbADiWoeqElypqalZfrHJ/LSY7AnSnOjTpw8A4MKFCzhx4oTS4/7+++8AgOrVq6N69eo5bjsn9PT0YGNjAwDYu3ev0jJz5sxRWT+rpxu1tbWhrq4OIHfXJD/c3NwAAGfOnBEXgM8sKSkJixYtynP7sqfhVI1gnDp1ap7b/paZm5tj+vTpADI6DBMmTMj2xsj58+cxZsyYrxEeEREREZFo9uzZ0NfXx8ePH+Hq6prtKKJ3796hR48e2Y6eyilZnwJQ3q94//49Zs+eXSDHyqww+9yyftqjR4/k7knICIKAuXPnqqwvi/3mzZtYvXp1lsdKSEiQGzWWVZ8V+F9fPjd91ho1aqBq1aoAMj5PmadSlTl69KiYeOvbt2+O286pmjVrAgAuX76sNPkXHh4uPqD8uS9xTfKjVq1asLW1BQDMnz9faV9y69ateR7xKfudu3v3rtIRkmFhYUo/l9+DyZMni2tv9u3bF7du3cqy/MePH/Hzzz/jxo0bXyM8+o4w8UdElEcvX77E9OnTsXnzZgBAw4YN0ahRI7kyc+fORalSpRAdHY1GjRrh77//xvv378X9b968wd69e9G9e/cC/+I6YMAAtG7dGoIgoHv37pg9e7bcNCAJCQkIDAzEyJEjUbFixQI9NgAxobZt2zali5hHRUWhUqVKmD17Nq5duyb3JOL169cxYMAAABmJtBYtWuT4uD169BDXhOvVqxe2b98uLoQeGRmJHj16iIuP+/j45O3ksiF7L//66y+sWrUKHz9+BJDxVNyPP/6InTt3qnwqrly5cpg2bRouXrwo13m4f/8++vfvj8TERKipqaFdu3ZfJPbP9e7dG9WqVYMgCHB1dcWBAwfETlhERARcXFzyNYVH+/btAWR07vbt2yd+DiIjI9GvXz/s2rVLbhTl92Tq1Kno3bs3AGDp0qVo0qQJ/Pz85NbKeP/+PQ4fPgxXV1c0a9Ysy6dTiYiIiIi+hMqVK+Pvv/+GlpYWbt26hVq1amHBggVyM3ekpaXh2rVrmDlzJipWrKgygZIX9vb2sLa2BgAMHjwYoaGh4r4LFy7AyckJ7969K7DjyRRmn7tZs2Zo06YNAOCnn36Cr6+v2H+MiopC//79cfbsWZX9zhYtWmDQoEEAgJEjR2LcuHF4+PChuD8pKQkXL17E5MmTUa5cObx+/Vrc98svv6BXr17Yu3ev3PYPHz5gzZo12LJlCwCgU6dOuTqnBQsWAADOnj0LNzc3cSmVlJQUbNu2TexnN27cGN26dctV2znRuXNn6OvrIyUlBb169UJERIR4/AMHDqB169Zyszx9HnuHDh3w999/y80KlZSUhF27dmHhwoUAcn9N8koikcDb2xsA4O/vD3d3d/Gz+enTJ2zcuBHDhg3Lc1+7bdu2UFNTQ0xMDPr37y8m+5OTk7Fr1y60bdsWBgYGBXMy3xhzc3Ps3bsXhoaGeP78ORo0aIBff/0VN2/eFBOwgiDgzp078PHxgY2NjbjWJlGBEoiISClPT08BgABAsLKyknsZGRmJ+wAIDg4OwrNnz5S2c/v2baFy5cpiWTU1NcHU1FTQ09OTa6N169Zy9SIjI8V9kZGRKuMsV66cAEDYtGmTwr64uDjBxcVF7jiGhoaCsbGxIJFIxG0aGhoKdTdt2iQAEMqVK5enY//9999i+5qamkLp0qWFcuXKCU2aNFE4PwCCurq6YGpqKmhpaYnbtLS0hN27d6s8vipRUVFCtWrV5NoxNjaWew+WLVumtG5Ozjs779+/F6pWrSp3PNnxNTU1hR07dqi8dpmviZqammBiYiJIpVJxm0QiEZYuXapwzKzeC1ndwMBAlTG3aNFCACB4enoq7AsPDxdKlCghtqOtrS3+DmhrawuHDh0S9124cCFX1+rRo0eClZWV3Gcx8+/X3Llzs4wtv+eWHdl1lUqlCn8HPn/Vq1dPZXx5ObYgCEJ6errg7e0t6OjoyH02DAwMBAMDA7ltpqamwpYtW/J0HCIiIiKi/Dp37pxga2sr9x1VS0tLMDU1FdTU1OT6NH379hWSk5PFujnph2XVRz506JCgoaEh7tfV1RV0dXUFAIKenp4QEBCgsu+QVbvZ9cvz0+fOqg8n4+7uLgAQ3N3dFfa9ePFCsLOzk+t3y/qdampqwrp16wRra2sBgLBjxw6F+klJScKPP/4oF7u+vr5gYmIi934BEKKiohRiylwnc38bgNC0aVPhw4cPKs9LlSVLlshdN2NjY7l7BFnde8nJ9czOhg0b5I5vYGAgHr9hw4bCypUrlX5OM98/AiDo6OgIpqamcm3Z29sLL168kKuX1ede1maLFi1UxhsYGCi2r8zYsWPlfu9MTEwETU1NAYDQqlUrYdq0aQIAoV27drm9VMKUKVPkztnIyEhsu0KFCsK2bdtUxlYQ55YV2XUFIJibm2fbl//nn3+UxpeXY8vcvn1bqFOnjtw10tDQEExNTeX+Vsmuf3R0dJ6PRaQMR/wREeXAq1ev5F6JiYkoUaIE2rVrh/Xr1+PKlSviUP7P2dvb4/r161i7di3atm0Lc3NzxMfHQxAE2NraomfPnli3bh127dpV4HEbGhri0KFDOHr0KHr37g1ra2skJSUhMTERpUuXRtu2bTFv3jzxSbaCNGDAAPz9999o2rQpdHV18eLFCzx+/Fh8+q106dI4ePAgxo0bh4YNG6JkyZL48OEDNDQ0ULVqVYwcORI3b94UpzDJjdKlS+PKlStYsmQJGjZsCB0dHSQmJqJs2bIYOHAgQkNDMXr06II+ZZG+vj7OnTuH8ePHo0KFCtDQ0ICmpqY42lA2HakyJ06cwLRp09CsWTOULVtWHC1oa2uLQYMG4fLlyxg7duwXi10ZOzs7XL9+HaNHj0b58uUhCAKkUil69eqFixcviutDAICxsXGu2i5XrhyuXLmCIUOGiL9DUqkULi4u8Pf3x7Rp0wryVPLs06dPCn8HPn+9efOmwI8rkUgwc+ZMPHz4EHPnzkWrVq1QqlQpJCcnIzU1FeXKlUO3bt2wYcMGPHr0CAMHDizwGIiIiIiIcqJJkya4c+cOduzYgf79+8PW1hZSqRTv37+HqakpmjZtit9++w3h4eHYvn07NDU1C+zYLi4uOHPmDDp16gRjY2OkpqbC3NwcgwYNQmhoKJydnQvsWJkVZp+7RIkSuHz5MmbMmIEqVapATU0NGhoa6NixI06fPo2ffvpJnE5VWT9NS0sL69evR0hICDw8PGBjY4O0tDR8+PABlpaWcHJywsyZM3H9+nWULl1arDdjxgwsX74c3bt3h52dHTQ0NMQ6bdq0wV9//YWgoCCVo+OyMm7cOFy5cgUDBgxA2bJlkZiYCB0dHTRs2BBLly7F5cuXVd57KQhDhgzBkSNH0KpVKxgaGiI1NRWVK1fG/PnzERwcrPKchg4dinXr1qFv376oXr06dHV1ER8fDxMTEzRr1gx//PEHrl69ihIlSnyx2JVZunQp9u3bBycnJxgYGCApKQn29vZYuHAh/P39kZCQACD3/XggYwrRLVu2wNHRETo6OkhJSYGtrS1+/fVXXLt27Yu+T7kRHR2dbV9edt+lINnb2yM0NBSHDx/GkCFDYGdnB319fcTHx8PQ0BD169fHuHHjEBoaiuPHj8PMzKzAY6Dvm0QQOI6UiIiI8u7kyZNo27YtpFIp4uPjC7QDT0RERERERLl37949VK5cGQDw5MkTlC1btpAjoqKmSZMmCAkJwe+//44ZM2YUdjhEVIA44o+IiIjyTBAEcS2GVq1aMelHRERERERUBMybNw8AULVqVSb9SEFwcDBCQkIAAO3bty/kaIiooDHxR0RERFkKDAzE2LFjceXKFXEKDEEQEBoais6dO+PUqVOQSCSYPHlyIUdKRERERET0fbhz5w5+/PFHnDlzBu/fv5fbPmjQIGzatAkAMHXq1MIKkQrZyJEj4evri5cvX0I26V9sbCzWrl2Lrl27Ash4gLd+/fqFGSYRfQGc6pOIiIiytH//fnTv3l38t4mJCT5+/IhPnz4ByFiHbtGiRRg/fnxhhUhERERERPRdCQsLQ+3atcV/GxkZISUlBYmJieK20aNHY9myZYURHhUBtWrVwn///QcA0NbWhq6uLmJjY8UkYNWqVXHixAm5NRyJqHhg4o+IiIiy9PLlS2zYsAGnTp3Cw4cP8ebNGwiCgFKlSqFZs2b45ZdfUK9evcIOk4iIiIiI6Lvx/v17rFu3DgEBAYiIiMDr16+RmpoKS0tLNGrUCEOHDoWzs3Nhh0mF6ODBg9i/fz8uXbqEV69eIS4uDoaGhqhWrRpcXV0xdOhQ6OrqFnaYRPQFMPFHREREREREREREREREVAxwjT8iIiIiIiIiIiIiIiKiYkCjsAMgKirS09Px/PlzGBgYQCKRFHY4RERERPkiCALev3+PUqVKQU2Nz/sREVHRwf43ERERFSdFrf/NxB/R/3v+/DnKli1b2GEQERERFainT5+iTJkyhR0GERGRiP1vIiIiKo6KSv+biT+i/2dgYAAg45fT0NCwkKMhIiIiyp/4+HiULVtW/I5DRERUVLD/TURERMVJUet/M/FH9P9k04sYGhqy40FERETFBqdQIyKioob9byIiIiqOikr/u/AnGyUiIiIiIiIiIiIiIiKifGPij4iIiIiIiIiIiIiIiKgYYOKPiIiIiIiIiIiIiIiIqBhg4o+IiIiIiIiIiIiIiIioGGDij4iIiIiIiIiIiIiIiKgYYOKPiIiIiIiIiIiIiIiIqBhg4o+IiIiIiIiIiIiIiIioGGDij4iIiIiIiIiIiIiIiKgYYOKPiIiIiIiIiIiIiIiIqBhg4o+IiIiIiIiIiIiIiIioGGDij4iIiIiIiIiIiIiIiKgYYOKPiIiIiIiIiIiIiIiIqBhg4o+IiIiIiIiIiIiIiIioGGDij4iIiIiIiIiIiIiIiKgYYOKPiIiIiIiIiIiIiIiIqBhg4o+IiIiIiIiIiIiIiIioGGDij4iIiIiIiIiIiIiIiKgYYOKPiIiIiIiIiIiIiIiIqBhg4o+IiIiIiIiIiIiIiIioGGDij4iIiIiIiIiIiIiIiKgYYOKPiIiIiIiIiIiIiIiIqBhg4o+IiIiIiIiIiIiIiIioGGDij4iIiIiIiIiIiIiIiKgYYOKPiIiIiIiIiIiIiIiIqBhg4o+IiIiIiIiIiIiIiIioGGDij4iIiIiIiIiIiIiIiKgYYOKPiIiIiIiIiIiIiIiIqBhg4o+IiIiIiIiIiIiIiIioGNAo7ACIipqwsDDo6+sXdhhERPSVmZubw9raurDDICIiIvpusP9NRFR0sY9M9O1i4o/oMy1atCjsEIiIqBDo6OriTng4OzZEREREXwn730RERZeujg7C79xhH5noG8TEH9FnWv+yDpY2dQs7DCIi+opiosJxfPEAREdHs1NDRERE9JUsqOsIBxOzwg6DiIg+cz8+DqP/DWEfmegbxcQf0WdMSleBlW2dwg6DiIiIiIiIqFizMTCEg4lpYYdBREREVKyoFXYARERERERERERERERERJR/TPwRERERERERERERERERFQNM/BEREREREREREREREREVA0z8ERERERERERERERERERUDTPwRERERERERERERERERFQNM/BEREREREREREREREREVA0z8ERERERERERERERERERUDTPwRERERERERERERERERFQNM/BEREREREREREREREREVA0z8ERERERERERERERERERUDTPwRERERERERERERERERFQNM/BEREREREREREREREREVA0z8ERERERERERERERERERUDTPwRERERERERERERERERFQNM/BEREREREREREREREREVA0z8ERERERERERERERERERUDTPwRERERERERERERERERFQNM/BEREREREREREREREREVA0z8ERERERERERERERERERUDTPwREREREREREREREVGBmDx5MiQSCSQSCWbPnq2w38vLS9yv6nXnzh2FeqmpqZgxYwbKli0LbW1tODg4YPfu3SrjCAsLg6amJoYOHVqg50dU1GkUdgBERERERERERERERPTtCwkJweLFiyGRSCAIQpZla9asiVq1aindZ2RkpLBt6tSpWLx4MSpWrIhOnTohMDAQvXr1wu7du+Hm5iZXNi0tDT/99BPMzc3h4+OT5/Mh+hZxxB8REREREX3XTp48iUGDBqFy5cowNDSEtrY2SpYsiTZt2mDp0qV48+ZNYYdI9E0oX748JBIJHj16VNihFKlY8sPJyQkSiQRBQUH5aicoKAgSiQROTk4FEhcREZEyiYmJ8PDwQMmSJdG1a9dsy3fr1g2+vr5KXyVLlpQr+/r1a6xYsQJVq1bFjRs3sG/fPly4cAGamprw8vJSaHv58uW4cuUKli9fDmNj4wI6Q6JvAxN/RERERET0XYqOjkabNm3Qtm1b+Pr6IiUlBS1btkSPHj1gb2+PkJAQjB8/HhUrVsSlS5cKO1zKo+KSAKJvS0El7L4G2ZRqRERE+TVt2jTcu3cP69atUzpiLz9u3LiB5ORk9O/fH7q6ugAAOzs7tGjRArdu3UJ8fLxY9smTJ5gxYwZcXFzQs2fPAo2D6FvAxB8RERFRPmW3fgEAvH37FtOmTYODgwP09PSgpaWFMmXKoGfPnjhz5ozSOgkJCRg5ciSsrKwglUrRoEEDnD59WmUchw8fhkQiwdy5cwvkvIiKs7i4ODRt2hQBAQGws7PDmTNnEBkZiQMHDmD79u04ffo0YmJisHbtWujr6+PFixeFHTIRfYe2bNmC8PBwODo65qsdR0dHhIeHY8uWLQUUGRERkbygoCCsWLECP/zwAzp27Fjg7b99+xYAYGpqKrfdzMwMAPDhwwdx28iRIyGRSLBq1aoCj4PoW8A1/oiIiIjyISfrFzx48ADNmzfH8+fPYWZmBicnJ+jq6uLWrVvYs2cP9uzZg8WLF2P8+PFy9dzd3bF3715Ur14dDRs2xMmTJ9GuXTucPXsWDRs2lCv74cMH/Pzzz3BwcMCkSZO+2PkSFRejRo1CREQEypcvj/PnzyvcQAAAbW1tDB06FF27dkVsbOzXD5KIvnvW1tYF0o6uri7s7OwKpC0iIqLPffjwAYMHD4aVlRX++OOPHNe7evUqpk6dipiYGBgZGaF27dro3LkzDAwMFMqWL18eABAeHi63PTw8HFpaWjA3NwcA7Nq1C4cPH8ayZctQtmzZPJ8T0besyI/4kz09n5uXbM76b2lqjW8Nry0REVHO1y8YP348nj9/jk6dOuHx48c4cuQIdu/ejdu3b2Pt2rUAgClTpiAqKkqsExYWhr1798LZ2RlhYWE4cOAADh06hNTUVKWjCn/77Tc8e/YM69atg6amZsGfLFEx8vDhQ2zfvh0AsGTJEqVJv8ysrKxQpUoVhe3//PMPnJ2dYWpqCm1tbZQrVw6DBw/G3bt3lbaTecrJwMBAtG3bFiYmJtDR0UGdOnVUjsSJi4vD9OnTxRHD2traKFWqFJo0aYKZM2ciJSVFLJuTdbxUTeuXefvWrVvh6OgIfX19WFhYoG/fvnjy5AkAQBAErFy5ErVq1YKenh7Mzc3h4eGB169fqzzm3bt3MWzYMNjY2EAqlcLIyAjNmzfH1q1b833Oqvj6+kIikeDx48cAgAoVKsj1Gz/vy/z777/o1asXSpUqBS0tLVhaWqJz5844efKkymOkpqZizZo1aNy4MYyMjCCVSlGpUiWMHj0az549yzbGz2X+jBw7dgxOTk4wMjKCiYkJXFxccOPGDbHs9u3b0ahRIxgYGMDY2Biurq548OCByuvg4eGh9JiPHj2CRCIRb6Z97t27d/j9999Rr149GBkZQUdHBxUrVkSvXr1w7NgxleeSm894Tpw6dQqurq4oWbKk+P50794dFy5cyFN7e/bsQfv27WFhYQEtLS2ULl0aAwYMwO3bt1XWycm1kP0OBgcHAwBatmwp97nz9fUFIH/d09LSsGTJEtSuXRv6+vpyv5/Z9b1Pnz6Nnj17okyZMtDW1oaFhQXq168PT09PcWRE5rgy/23w8vKSO9bn91YePXoET09PSCQSDBs2TOV1+ffffyGRSFC6dGmkpqaqLEdERMXXxIkTERkZidWrV8PExCTH9Q4dOoQFCxZg/fr1WLRoEfr374+yZcsq/c5Qq1YtlCtXDps2bcKJEycQHx+PJUuW4Pr162jfvj20tLQQGxuLMWPGwNHREb/88ktBniLRN6XIj/hzd3dX2Pby5Uv4+/ur3M+n2IiIiOhrkK1fcOTIEezatUtlOdn0nJ6entDT05PbN3ToUCxatAj37t3D5cuXUaZMGQBAaGgoAGDw4MFQV1cHADg7O8PW1hYhISFybVy+fBkrV67Ezz//rDASkIgUHT58GGlpaTA2NkaXLl1yXV8QBHh4eGDLli3Q0NBA8+bNYWlpiatXr2LTpk3YuXMn9u7di/bt2yut/9dff2H27NmoU6cO2rdvj0ePHuHixYtwd3dHTEwMxo4dK5ZNTExE06ZNcfPmTVhYWMDZ2Rl6enp4+fIl7ty5I65DaGxsnMeroWjatGlYtGgRmjdvjg4dOuDff//FP//8g/Pnz+O///7D8OHDcfDgQTg5OaFixYo4f/48Nm/ejGvXruHy5cvQ0tKSa2/37t344Ycf8OnTJ9jZ2aFjx46Ii4vDpUuXMHDgQJw+fRp//fVXgZ+zra0t3N3dsWfPHiQkJKBHjx7Q19cX95coUUL8ef369Rg+fDjS09NRu3ZtODk54fHjxzh8+DAOHz4MLy8veHp6yrWflJQEFxcXBAQEQCqVomXLljA0NERISAhWrFiBHTt2wN/fH3Xq1Mn1e7B27VosWLAAjRs3Rvv27REWFoYjR47g/PnzuHLlCtauXYulS5eiefPmaN++PS5dugQ/Pz9cunQJN2/ezNUNt6z8999/6NSpE549ewYjIyM0bdoUBgYGePLkCQ4fPozXr1+jQ4cOCvVy8xnPiYkTJ2Lx4sVQU1NDvXr10KxZMzx58kR8KGb9+vUYNGhQjtpKTU1F//79sWvXLmhra6Nu3booXbo07t69i23btmHfvn3Yt2+fwu9vTq9FiRIl4O7ujuPHj+PVq1do166d3GfN1tZWrl1BEODq6orjx4+jWbNmsLe3x61bt3J0LqNHj8aKFSsAZNwMbdasGeLi4hAREYHff/8dLVu2zPIhgFq1asHd3R2bN28GoHh/RV9fHyNGjMD8+fOxbds2LFiwQOnv3Z9//gkAGDZsGDQ0ivxtJiIiKmAnTpzA2rVr0adPH3Tr1i1HdWxsbDB37lx06NAB5cqVAwDcvn0b8+fPx+HDh+Hu7g51dXX0799frKOlpYUVK1bA1dUV7dq1E7eXLFkSS5cuBZDxQG10dDROnDgBNbX/jXlKTEwU1wUk+h4U+W9ksqfhMgsKChITf8r2y2zZsgWJiYkFNjUG/Q+vLRERfe8+X78gq8SfVCqVW29AFdnUJEDW6xfIRq8AGTcwhw4dilKlSnFtP6IcunLlCgCgTp06YmI9N9auXYstW7bA3NwcJ0+eRK1atQBk3MD39vaGt7c3+vbti7t378LCwkKh/vz583Hw4EG4uLiI23x9fTFo0CB4eXlh2LBh0NHRAZAxKunmzZvo0KEDDhw4IDeiNz09HWfPni3wmxjr16/HlStXULNmTQDAx48f0bZtW5w7dw4tWrRAYmIi7ty5I96kiY6ORqNGjXD9+nXs3r1b7gbNjRs3MHDgQEgkEuzduxeurq7ivsePH6Nz587YtGkTnJyc8MMPPxToOTdt2hRNmzZFUFAQEhISsGjRIqWj2m7cuIGff/4ZgiBgy5YtGDhwoLjv2LFj6NatG7y8vNC4cWO0adNG3Ofp6YmAgADY2NggICBAbDslJQUjRozAxo0b4ebmhjt37igkQ7OzdOlSnDx5Es7OzgCAtLQ09O3bF7t370a3bt3w4sULufcoMTERbdq0QUhICFatWoXffvstV8dTJiEhAZ07d8azZ8/www8/4M8//5RLnMbFxeHy5ctK6+bmM56d9evXY/HixbC1tcXevXtRo0YNcd+ZM2fg4uKC4cOHo2nTpqhUqVK27Xl6emLXrl1o0KABduzYgQoVKoj79uzZgz59+qBfv354+PChmOTKzbWws7ODr68vnJyc8OrVK0ydOjXL5NuTJ0+Qnp6OGzduoHLlyjm6JgCwYsUKrFixAmZmZti9ezdatmwpt//ff/9FyZIls2yjW7du6Natm5j4U3V/pWfPnti2bRs2bdqEcePGye2Ljo7Gzp07oampiaFDh+Y4fiIiKh7i4uIwZMgQWFhYiA+j5ETm71syTZo0waFDh8QHW8aNG4eePXvKfY/q3LkzwsLCsH37drx58waVK1fG4MGDYWpqinPnzmH9+vWYOnUqHBwckJaWBm9vb6xevRrR0dEwNDTEgAEDsHDhQiYBqdgr8lN95oe1tTXs7Oz4i/wF8NoSEdH3LLfrF8hGQ3h7eyMxMVFu3/r163Hv3j04ODigUaNG4nZl6xekpKTg/v37KF26tLhtyZIlCAsLw59//ql0HQQiUvTmzRsAgKWlZZ7qL1q0CAAwc+ZMMekHZEyT5+npiRo1aiA2Nhbr169XWn/UqFFyCREA8PDwgJ2dHeLi4sTEJAC8evUKANCmTRuFaXzV1NTQokWLXCeVsvP777+LCSUA0NHREdcgvXHjBpYvXy4m/YCMhxZGjBgBIGM6xszmzJmDpKQkzJ49Wy7pBwDlypXDxo0bAQDLly8Xt3/tc162bBlSU1PRvXt3hZtQHTp0EJMZCxcuFLd/+vRJHOW0dOlSuYSipqYmli9fDisrK0RGRmLPnj25jmn06NFi0g8A1NXVMW3aNADAzZs3Fd4jXV1dTJgwAYDie5BXGzZswNOnT1GrVi389ddfcokuADAyMkLr1q2V1s3NZzwr6enp8PLyApAxtW7mpB8ANG/eHDNmzEBycrI4dXZWYmJisHTpUkilUuzdu1cu6QcAbm5uGDZsGN69eyc3DW1+rkVOzJ07N1dJv9TUVMyaNQsAsG7dOoWkHwA4OjoW2LpGY8aMAQCsXr1aYT3jDRs2ICkpCW5ubnIjGz+XlJSE+Ph4uRcREX37xo4di6ioKKxcuVLuQdb88PLygrq6Ot68eYNLly4p7K9WrRrmzJmDdevWYeLEiTA1NUVycjKGDh0KGxsbzJw5EwAwadIkzJo1C127dsXBgwcxbNgwrF69WmnSkai4KdaJP1Vz4Xt4eIhz60dERKB3796wtLSEnp4e6tevjwMHDohlL126hC5dusDCwgI6Ojpo1KhRlh2pjx8/YvHixWjYsCGMjY0hlUpRpUoVTJ48WW5+/ZwKCAhA586dYWVlBU1NTZiYmKBSpUoYMGAAzpw5I1f2zZs3WL58OTp27IgKFSpAR0cHhoaGqFevHhYsWIBPnz7Jlb9z5w4kEglMTEwU9mVWr149SCQSueuSk2sbGRmJgQMHokSJEtDW1oaNjQ2mT5+OpKQkpcdJTU3F4sWLUb16dUilUlhaWqJnz564fft2lutS7N69G61bt4aZmRk0NTVhZmaGqlWr4qeffsL169ezucJERES5l9v1CxYuXIi6deviyJEjsLa2houLC3r16oVq1aph+PDh6NSpE/z9/eWmx2rVqhUMDAywePFiXL58GbGxsZg6dSrevn0rTk0YGRkJb29vuLm55Wm6QiLKvaioKHEdNWXLDkgkEnHKwcDAQKVtdO7cWel2e3t7AJBbG65+/foAAB8fH2zZsgUxMTF5Dz6HOnbsqLBNNpJKQ0MDbdu2Vbn/+fPn4rb09HRx3bPevXsrPVa9evWgr6+Pa9euiX2Sr33Osj6NqjXwhgwZAgA4e/Ys0tLSAGSMGv3w4QNMTU2Vvp+6urro06cPANWfg6xk9R5ktz/ze5Afx48fB5Bx/rkdGZubz3hWrl27hufPn8PGxgZ169ZVWkY2mu7zabCVCQwMxMePH9GkSRO5h2iyay8/1yInevTokavyoaGhePPmDczNzdG9e/cCj+dz9evXR6NGjXDv3j1x9iUg43d8zZo1AJDtOkrz5s2DkZGR+CqopCQRERUuPz8/aGhoYNWqVXBycpJ7yf7/uXHjRjg5OYnfjbJjamoqPqAXFRWVozrz5s1DeHg41q5dC6lUivfv3+PPP/9E48aNsWHDBnTu3Bk+Pj7o1asX9u3bp3JNbqLiolgn/rJz9epV1K1bF//99x+cnZ1Rs2ZNXLlyBd27d8eePXuwf/9+NGvWDFFRUXB2dkaVKlVw8eJFtG/fHufOnVNo7/nz52jQoAEmTpyIe/fuoX79+ujYsSOSkpKwcOFC1KtXT25qruxs3rwZbdu2xZEjR1ChQgX06NEDzZs3h6GhIf755x/s27dPrry/vz/GjBmD69evo1y5cujWrRscHR0RERGBqVOnolWrVnJJNzs7OzRq1AixsbHYv3+/0hhu3LiB0NBQWFlZoVOnTjmOPSwsDLVq1cLZs2fRokULNG/eHC9evMCcOXOU/pFPT09H9+7dxWvXokULtGrVClevXkX9+vXFdY4+9/vvv6NXr14IDg5G9erV0bNnTzRs2BDq6urYuHGjuKYSERFRQcnL+gVWVlYICgrCgAED8PbtWxw5cgS7d+/G7du3Ubp0abRq1UphOkBzc3MsWLAAz549g6OjI0xMTLBkyRLY29uLox9GjBghjizJ7PNRhUQkT/b79vr161zXlSUszMzMYGhoqLSMjY2NXNnPqZouX9Ze5ofynJycMGXKFLx+/Rru7u4wNzdHlSpVMHjwYBw4cADp6em5PofsKItPNsKpZMmSStfwko04zhz727dvxVE9ZcuWhUQiUXipqanhw4cPSE9PFx+U/NrnLHufPh/9JSN7Pz99+iTGmF2dzPVymuTKLKv3QNV+Ze9Bfsj6rnZ2drmum5vPeFYePnwIAHjw4IHSz49EIoGjoyOA/43kzUl7p06dUtler169FNrLz7XIjqWlZa5n0pHFU6VKFUgkkgKPSZnRo0cDAFauXCluO3z4MB4/fozatWujcePGWdafNm0a4uLixNfTp0+/aLxERPT1pKamIjg4WOElm8Xh0aNHCA4OxsWLF3PUXlpaGuLi4gAgR7PaREREYN68efDw8ECrVq0AZKwXmJycrPD/p6ZNmwLIuHdNVJwV+TX+vqQVK1Zg9uzZ+PXXX8UvyytWrMDo0aMxbtw4JCQkYOPGjXLDf8eNG4c//vgD3t7eOHnypLhdEAT06tULN27cwJAhQ7B06VLxD1NqaiqmTp2KxYsXY9CgQTlORnl7e0MQBJw9e1b8oyTz+vVrhQ5k3bp1ceHCBTRs2FBu+7t379CnTx+cOHECy5cvx6RJk8R9gwcPxoULF+Dr66s0Ibdp0yYAwIABA3K1SPeyZcvw22+/wdvbW3wi8ubNm2jYsCH279+PCxcuyE1ntnLlShw+fBglS5ZEYGAgqlSpAiDjD/2ECROwbNkyhWMkJSVh/vz50NfXx5UrV8Q6Mo8fP8bHjx9zHDMREVF28rp+wZ07d9C5c2e8efMGq1atQufOnWFoaIhr165h4sSJmDBhAo4fP45jx47JjSQYMWIEateujb179yI+Ph41a9aEh4cHdHV1sW3bNvj7+2Pt2rUoWbIkEhMTMWnSJGzduhXx8fHi1Huenp5fZHQC0besbt26+Pvvv3H16lWkpaV99d8RNbXcPX85f/58DB8+HIcOHcK5c+dw/vx5bNq0CZs2bUL9+vURGBgIPT29HLWVk6RZVvHlJvbMx1I2OvJz2tra4s8Fec7fouyuc24/Q1n5EsnjgopPFluJEiXQrl27LMvmZHoxWXu2trZo0qRJlmW/RJJPmZyudVjY3NzcMHHiRBw7dgyRkZGoUKGCON1tdqP9gIzf78y/40REVDzExsaq3Ofh4YHNmzdj1qxZmD59eo7bPHjwIBITEyGRSFCvXr0sywqCgGHDhsHIyAiLFy8Wt8vu9SckJMiVl/37az04Q1RYvuvEn6Ojo1zSD8i4webl5YWoqCj07NlTYc7f6dOn448//sCZM2eQkpIirjnh7++P8+fPo1atWlizZo1ckkxDQwM+Pj7w9/dHYGAgbt68ierVq2cb36tXr2BkZKSQ9AMyngr8fE0S2bQpnzMxMcGKFStQpUoV7N69Wy7x17t3b4wZMwYnT57Es2fP5KY7SUlJEdc1kE1XlFN169bFrFmz5K5t9erVMXDgQKxZswYBAQFyiT9ZYs/Ly0sugaeurg4fHx/s2bNHIdEZHx+Pjx8/okaNGgpJPwBy644ok5SUJDcCkmsMEBFRdmTrF+zcuTPH6xekpqaiR48euH//Pnbt2oWePXuK+1q0aIETJ06gatWqOHnyJLZs2aLw/9yGDRsqPNQTExODcePGoVmzZvjpp58AZDykc+DAAUycOBFNmzbF/v37MWvWLCQkJMh1gIgIcHFxwfjx4xEbG4uDBw/maqo82fdl2Wg2ZaP+ZKOKVE0lmBfly5fHqFGjMGrUKADA5cuXMWDAAFy+fBk+Pj7w9vYGAHHtu/fv3yttJzczkOSXubk5dHR08PHjRyxatCjX677k9Jzzq3Tp0njw4AEePnyotJ8mez+lUilMTU3FOkDGlMuqfInPQV7k9TNhbW2N8PBw3LlzJ1/r1+WHbDpIMzMz+Pr6Flh7VapUyVV7ReFafB4PANy9exeCIHyVm5caGhoYMWIEpk+fjlWrVuGnn37CyZMnYWpqir59+37x4xMRUfHx5MkTnDlzBm5ubpBKpXL79u/fjx9//BEA0L9//yzXjwUyphENDg7Gtm3bxO9pAFC1alVoa2vDz88Ps2fPhqmpKRITE8V73bVr1y7gsyIqWr7rqT47dOig8AVZQ0NDnK5F2boJZmZm4oKhmdfsO3LkCICMufmVjYxTU1ND8+bNAeRs7QEgIzEZFxeHH374AaGhoTl6EjMtLQ2nTp3CrFmz8PPPP2PQoEHw8PDAnDlzAGQMfc7MwMAAbm5uSE9Px5YtW+T2HTlyBG/evIGjoyOqVauWo5hlXFxclHY+lK3pEBUVJXaK+/Xrp1BHS0sLbm5uCtstLCxQvnx5XL9+HRMmTMDt27dzFSPXGCAiotzKy/oFly5dwu3bt6GtrQ1XV1eFNk1MTNChQwcAGWv75sSECRMQHx+PdevWQSKRICIiAn5+fujTpw8WLFiAzp07Y+PGjWjQoAFWrlyJDx8+FNAVICoebGxsxBvVEyZMyHYNudevX4vfo8uUKSNO4agscSAIgri9ZcuWBRf0Z+rXr4+ff/4ZgPxURbIk08OHD5GcnKxQT9Zv+RrU1dXRpk0bAMCuXbvy3Z6qc86OLPGVmpqqdL9sTTdViaC//voLANCsWTOxrydbmzAmJgYHDx5UqPPx40f8888/AL7s5yAnZJ+JO3fuKN2v6jPRvn17ABnnL1vb8GurX78+zM3Ncfv2bdy6dSvf7Tk7O0NLSwtBQUG5muo3L9ciu89dftSrVw/m5uZ48+aNymU7ckP2QHN2sQ4bNgxSqRR//fUXFi9eDEEQMGTIkG9m1CIRERUNMTExGDhwICwsLNC8eXP07dsX3bp1Q+XKldG9e3fExMSgZcuWWL16dZbtvHr1CpMnT0b79u0V7inr6+tj/PjxePnyJapVqwY3NzdUr14dN2/eRN++fWFra/slT5Go0H3XiT9V6w7I1k5QtV/Z2gmyxNWMGTNUrhWwatUqADlbewAAVq1ahYoVK+Lvv/9GvXr1YGxsDGdnZ8yZMwdPnjxRKH/v3j3UrFkTrVu3xsyZM7F69Wr4+vpi8+bNYlJP2ai2wYMHA1Ds6Mqm+cztaD8gd2s6yBZpNTc3l1u3IrPy5csr3b5lyxZYWlpiyZIlqFatGszMzNCxY0csXboU0dHRWcbINQaIiCgvcrt+gez/2bq6uiqnEzQyMgKAbJMPABAYGAhfX19MmzZNnIZMdgNc2foFycnJuX44huh7sGLFCtja2iIyMhJNmzZVuoZ3cnIy/vrrL9SuXRvh4eHi9okTJwIAZs2ahf/++0/cLggCZs+ejbCwMBgbG4sjcvPDz88PZ86cUXgIMCUlRXzgIPNMF+XKlUOlSpUQGxuLBQsWyNUJCgrCzJkz8x1Tbnh6ekJLSwuTJk3C5s2blT7MePPmTbn1y3N7ztkpU6YMAKhMHI0ZMwYaGhrYv3+/+BS4jGxdV+B/7zuQMfpv5MiRADKSx5lHzaWkpGDMmDF4+fIlKlSooPQhxq/J0dERhoaGuH37Nv7++2+5fbt371ZYJ1bmxx9/RJkyZXDt2jX89NNPClNlxcfH5/iBlbzS1NSEp6cnBEFA9+7dlf6epqWl4fTp0zlaN8jKygqjRo1CQkICOnfujBs3biiUSUpKwsGDB+USpXm5Ftl97vJDQ0MDv/32GwBg6NChOHPmjEKZy5cvi33t7OQ0VnNzc/Tr1w8xMTFYt24d1NTUxGQ8ERFRTpUtWxZTpkyBo6MjHj9+jEOHDuHo0aP48OEDXFxcsH37dgQEBKi8TywzduxYJCcnq0wQzpkzBz4+PtDT08OBAweQnJyMKVOmFMgsAkRF3Xc91WdBrpsg65Q2bdpUfAJYlZyOnrO3t0dERAROnDiB06dPIyQkBGfPnsXp06fx+++/Y+PGjRgwYIBY3s3NDbdu3YKLiwsmT56MqlWrwtDQEJqamkhOTlY5n37z5s1hY2ODu3fvIiQkBI0bN8br169x9OhRSKVSpWv/ZScvazpkNT2Jqn3NmjXDo0ePcOTIEQQHByMkJAT+/v44duwYPD094efnB2dnZ6V1ucYAERHlVl7WL5CNtHj37h3u3buHSpUqKdS9dOkSAIizDqiSlJSE4cOHw97eHtOmTRO3c/0CotwzMTHB+fPn0bt3bwQFBaFZs2aoUKECatSoAV1dXbx69Qr//vsvPnz4AENDQ5QqVUqsO2zYMISEhIgP6LVo0QKWlpa4evUqIiIioKOjg+3bt8PCwiLfcQYHB2PZsmUwNzdH7dq1YWlpiffv3+PixYt4/fo1SpcujcmTJ8vVmT9/Ptzc3DBz5kzs27cPlSpVwsOHD3H16lXMmDEDv//+e77jyqk6depg69at8PDwgIeHB6ZPn46qVavCwsICMTExuHHjBqKiotC7d29xVHRezjkrPXr0QGBgIAYMGIC2bdvCxMQEADBp0iRUqVIFDg4O+PPPPzFixAgMHDgQS5cuhZ2dHR4/foyQkBAIggAvLy+0bdtWrl1vb29cuXIFp06dgr29PVq2bAkDAwNcuHABT548gZmZGXbv3i2O/CosOjo68Pb2xrhx4/DDDz9g9erVKF26NMLDw3H79m1Mnz4ds2bNUqinr6+PgwcPomPHjti0aRP8/PzQpEkT6Ovr4+nTp7h27RocHR2/+NSXv/zyC548eYKFCxeiWbNmqFatGmxtbaGjo4OXL18iLCwMsbGxWL16tcLU2MrMnz8fL168wPbt21GrVi3UrFkTFStWhIaGBqKiohAWFoaEhAQcO3ZMfMAmL9eiR48e2LRpEyZPnoyAgABYWlpCIpFg8ODBCg/q5MWYMWMQERGBNWvWoEWLFqhduzaqVKmC+Ph43LlzBw8fPkRgYKCY1MtKjx49sGjRIrRu3RqtWrUSH3ZesGABzMzM5MqOHj1aHAXbqVMnlQ/pEhER+fr6Kk2ymZmZYf78+fluf8eOHVnul0gkmDRpktyyV0Tfi+868VeQZNNEdu3aVe5J0PzS0NBAx44dxWlH4+PjsWTJEnh7e2PYsGHo3r079PT0cOfOHVy/fh2WlpbiNGSZ3bt3T+UxJBIJPDw8MGPGDGzatAmNGzfG1q1bkZqail69esHY2LjAzkcZ2Q3RN2/eICEhAXp6egplHj16pLK+jo4O3NzcxCdp37x5g+nTp2PdunUYPHjwV13HhIiI6HONGjVC6dKl8ezZM/z444/Ys2ePmAxIT0+Hj48PLly4AADZrpEza9Ys3Lt3D2fPnpW7kVynTh0AwLZt2zBq1Cjo6OggOjoa+/btg1QqRdWqVb/Q2RF92ywtLREYGIjjx49jx44dCAkJwalTp5CUlAQzMzM0atQInTp1wsCBA+XWDJFIJNiyZQs6dOiAdevWITQ0FAkJCShRogQ8PDwwdepUpWtQ54WHhwd0dHRw7tw53L59G8HBwTAyMoK1tTXGjh2LoUOHKtyYd3V1xeHDhzF37lxcu3YN9+7dg4ODA/755x/06tXrqyb+AKBnz56oX78+li9fjpMnT+L8+fNIS0uDlZUVbG1t8csvv8iNisvLOWdlxIgReP/+PbZu3YqjR4+Ks48MGDBAfJ+GDh2KmjVrYtGiRTh37hyuX78OIyMjdOzYEWPGjBGnLM1MW1sbx48fx/r167FlyxacPXsWSUlJKFu2LEaNGoUpU6YU+vp+MmPHjoWpqSmWLVuGa9eu4datW6hXrx7++OMP2NraKk38ARnr39y4cQPLli3DgQMHEBQUhPT0dJQsWRJdunTJ0+wweeHj44Nu3bph1apVOHfuHI4fPw4tLS2ULFkSTk5OcHFxUTqdtjIaGhrYtm0bBgwYgA0bNuDSpUu4efMm9PT0ULJkSXTu3BldunQRl+qQye216NSpE9avX4/Vq1fj9OnTSExMBJDxsHBBJP4kEglWr16Nrl27Ys2aNbh48SJu3rwJY2NjVKhQAe7u7qhRo0aO2po1axbU1NSwb98+7N+/X5wmePr06Qq/azVr1kSJEiXw8uVL/PLLL/k+DyIiIiIqeBJBEITCDiK3goKCxHUSsgrfyckJwcHBCAwMFNdtAP73RP6mTZvg4eGR43oy5cuXx+PHjxEZGSk+3ebn5wdXV1c4Ojri4sWLX/zJehMTE8TGxuLq1auoXbs2QkJC0KRJE9SqVQvXrl1TKD9t2jTxSQpl1ywqKgrlypWDvr4+Xrx4gQYNGuDmzZs4efKk0ic483ptfX19MWjQILi7u8s98VGhQgU8evQI69evFxdwlUlOToatrS2ePn2qUE+VuLg4MWEZExMjPtWblfj4eBgZGaHnvGCUqd482/JERFR8vLp/FdvH1UVoaKiYRMstVSP+AOD06dPo3LkzEhMTYWhoiAYNGsDAwAD//fcfHjx4AAD49ddfxTV5lbl16xZq166NIUOGKJ3KpF+/ftixYwcqVKiAOnXq4Pz583j58iWmTZuGuXPn5umc6Nsm+24TFxcnTrdORESUHwEBAWjTpg2qVKmC8PDwPN/7kP0/ao9TazSwsCrgKImIKL9uvItBx4Bj+eojE31Pilr/+7te468gde3aFfXr18e///6LQYMGKV3H7927d1izZk2OFvdOTEzEkiVLlLZz9uxZxMbGQl1dXZy2o3LlylBXV8eNGzcQFBQkV/7QoUNYunRplscrU6YM2rRpg/j4ePz666+4efMmrK2t0apVq2xjLQijR48GkLEGyN27d8Xt6enpmDZtmtL19x4/fowNGzYoXbfw0KFDADISpEXhF42IiL5vrVq1wo0bNzBy5EiUKlUK586dw6FDh/Dx40d0794dJ06cyDLpJwgChg4dCgsLC5VTovj6+mLKlClISUnBwYMHoaurCx8fnyzbJSIiIsqptLQ0eHp6AgDGjx/PqcSJiIiIiihO9VlA1NTUsH//fnTq1AmbN2/Gnj17ULNmTVhbWyM5ORkPHz7EjRs3kJaWBg8PD4WpOD+XnJyMCRMmYNKkSXBwcEClSpWgqamJR48eiYuW//bbb+JUYebm5vjll1+wbNkyODs7o1mzZihVqhQiIiJw9epVTJ8+HbNnz87ymIMGDYK/vz+WLVsGIGPkQl7W6suL0aNH4+TJkzh27Bhq1KiBli1bwtjYGJcvX8bz58/x888/Y9WqVXLTmr179w4//fQTfv75Z9SqVUtcF+nevXu4du0aJBIJFi5cCHV19a9yDkRE9H1TtX6BTMWKFbFy5co8tS2RSHD+/Pksy2hpaWH+/PkFslYCERERkcymTZtw5swZXLlyBTdv3oSDgwMGDx5c2GERERERkQoc8VeASpUqhYsXL2LNmjVwdHREREQE9uzZg3PnzgEAhg8fDn9/f0il0mzb0tfXx5o1a9C7d28kJSXh5MmT2L9/P16/fg1XV1ecOnUK3t7ecnWWLl2KjRs3onbt2ggNDcXRo0ehq6uLf/75R+WaDZl169ZNXLtEtu7f16Kuro4DBw7Ax8cHNjY2CAwMREBAAGrUqIF///0XJUqUAJCR4JSxsbHBH3/8ARcXF8TGxuLo0aM4cuQIEhIS8MMPP+Dy5csYMmTIVzsHIiIiIiIiouImODgYvr6+iIqKQvfu3XH48OFsH2YmIiIiosLzTa7xR9+fVq1aITAwEHv37s3xou25xTX+iIi+XwWxxh9RUVPU1hggIiKS4Rp/RERFG9f4I8qdotb/5og/KjLCwsKQnJwsty05ORleXl4IDAyEpaUlOnbsWEjRERERERERERERERERFW2cm4GKjLFjxyIsLAw1a9ZEyZIl8e7dO9y4cQMvXryAVCrF5s2bczRNKhERERERERERERER0feII/6oyPjpp5/QuHFjPHjwAAcOHEBwcDCkUikGDx6M0NBQtG/fvrBDJCIiIiIiIiIiIiIiKrI44o+KjP79+6N///6FHQYREREREREREREREdE3iSP+iIiIiIiIiIiIiIiIiIoBJv6IiIiIiIiIiIiIiIiIigEm/oiIiIiIiIiIiIiIiIiKASb+iIiIiIiIiIiIiIiIiIoBJv6IiIiIiIiIiIiIiIiIigEm/oiIiIiIiIiIiIiIiIiKASb+iIiIiIiIiIiIiIiIiIoBJv6IiIiIiIiIiIiIiIiIigEm/oiIiIiIiIiIiIiIiIiKASb+iIiIiIiIiIiIiIiIiIoBJv6IiIiIiIiIiIiIiIiIigEm/oiIiIiIiIiIiIiIiIiKASb+iIiIiIiIiIiIiIiIiIoBJv6IiIiIiIiIiIiIiIiIigGNwg6AiIiIiIiIiIi+Pw/ex0NXQ7OwwyAios/cj48r7BCIKB+Y+CMiIiIiIiIioq9uSui/hR0CERGpoKujA3Nz88IOg4jygIk/IiIiIiIiIiL66oKDg6Gvr1/YYRARkRLm5uawtrYu7DCIKA+Y+CMiIiIiIiIioq+uVq1aMDQ0LOwwiIiIiIoVtcIOgIiIiIiIiIiIiIiIiIjyj4k/IiIiIiIiIiIiIiIiomKAiT8iIiIiIiIiIiIiIiKiYoCJPyIiIiIiIiIiIiIiIqJigIk/IiIiIiIiIiIiIiIiomJAo7ADICpq3j2LgKZUv7DDICKirygmKrywQyAiIiIiIiIiIso3Jv6IPhOwcmhhh0BERIVAR1cX5ubmhR0GERERERERERFRnjHxR/SZ4OBg6OtzxB8R0ffG3Nwc1tbWhR0GERERERERERFRnjHxR/SZWrVqwdDQsLDDICIiIiIiIiIiIiIiyhW1wg6AiIiIiIiIiIiIiIiIiPKPiT8iIiIiIiIiIiIiIiKiYoCJPyIiIiIiIiIiIiIiIqJigGv8ERERERERERHRVxcWFgZ9ff3CDoO+EnNzc1hbWxd2GERERMUeE39ERERERERERPTVtWjRorBDoK9IV0cH4XfuMPlHRET0hTHxR0REREREREREX92Cuo5wMDEr7DDoK7gfH4fR/4YgOjqaiT8iIqIvjIk/IiIiIiIiIiL66mwMDOFgYlrYYRAREREVK2qFHQARERERERERERERERER5R8Tf0RERERERERERERERETFABN/RERERERERERERERERMUAE39ERERERERERERERERExQATf0RERERERERERERERETFABN/RERERERERERERERERMUAE39ERERERERERERERERExYBGYQdAVNSEhYVBX1+/sMOgIsrc3BzW1taFHQYRERERERERERERkQIm/og+06JFi8IOgYowHV1d3AkPZ/KPiIiIiIiIiIiIiIocJv6IPtP6l3WwtKlb2GFQERQTFY7jiwcgOjqaiT8iIiIiIiIiIiIiKnKY+CP6jEnpKrCyrVPYYRAREREREREREREREeWKWmEHQERERERERERERERERET5x8QfERERERERERERERERUTHAxB8RERERERERERERERFRMcDEHxEREREREREREREREVExwMQfERERERERERERERERUTHAxB8RERERERERERERERFRMcDEHxEREREREREREREREVExwMQfERERERERERERERERUTHAxB8REREREREREREVGREREVixYgU8PDzg4OAADQ0NSCQSzJ49W2UdLy8vSCSSLF937txRqJeamooZM2agbNmy0NbWhoODA3bv3q3yOGFhYdDU1MTQoUML5FyJiIgKmkZhB0BEREREREREREQks3r1aixbtixPdWvWrIlatWop3WdkZKSwberUqVi8eDEqVqyITp06ITAwEL169cLu3bvh5uYmVzYtLQ0//fQTzM3N4ePjk6f4iIiIvjSO+CMiIiIiIiJS4cGDB5BIJFBTU8ObN2+Ultm6das4mmTr1q1Ky7x58wZqamqQSCR48OABAMDX1xcSiQQeHh55iq18+fKQSCR49OhRnup/DUFBQZBIJHBycirsUCgfnJycIJFIEBQUVNih0HeievXqmDhxIrZt24bw8HAMHDgwx3W7desGX19fpa+SJUvKlX39+jVWrFiBqlWr4saNG9i3bx8uXLgATU1NeHl5KbS9fPlyXLlyBcuXL4exsXE+z5KIiOjLYOKPiIiIiIiISAUbGxuULVsWgiAgODhYaZnAwEDxZ1WJkaCgIAiCgLJly8LGxuZLhPrdYnKRqPj58ccfsXDhQvTr1w92dnZQU/sytzBv3LiB5ORk9O/fH7q6ugAAOzs7tGjRArdu3UJ8fLxY9smTJ5gxYwZcXFzQs2fPLxIPERFRQeBUn0RERERERERZaNmyJbZs2YLAwECFad+AjMSThYUFtLW1s0z8ydqS6d69Oxo2bKh06rmcOHXqFFJSUlC6dOk81f8aHB0dER4eLt5QJyIqSt6+fQsAMDU1ldtuZmYGAPjw4QMMDQ0BACNHjoREIsGqVau+bpBERES5xMQfERERERERURYyJ/4+9/TpUzx8+BBubm7Q1tbGtm3b8PTpU5QtW1aunKxu5sSfkZFRnpN+AL6JkYO6urqws7Mr7DCI6Dty9epVTJ06FTExMTAyMkLt2rXRuXNnGBgYKJQtX748ACA8PFxue3h4OLS0tGBubg4A2LVrFw4fPoxly5Yp/H0nIiIqajjVJxEREREREVEWZMm68PBwvHr1Sm6fbCSfk5MTWrRoIbdN5tWrV+JN5cyJP1Vr/GWeujIxMREzZ86Evb09dHV1xZvUgOo1/jKvxxYWFgZXV1eYm5tDW1sbVatWxeLFiyEIgtJzTUhIwIwZM1CpUiVoa2ujVKlSGDx4MJ49ewYvLy9IJBKl616pktU0nKGhoejduzfKlCkDLS0tGBoaomLFiujRowcOHDiQo/adnJzEaxocHCyutSiRSOSuFQCkpqZizZo1aNy4MYyMjCCVSlGpUiWMHj0az549U3mMqKgojBo1CpUqVYJUKoWRkRGaNGmCtWvXIi0tLcfX4vr165BIJKhRo4bCPh8fHzHu27dvy+178uQJJBIJrK2tFep9/PgRixcvRsOGDWFsbAypVIoqVapg8uTJ4kimzFJSUrB161b0798fdnZ2MDQ0hI6ODqpUqYLRo0fj+fPnOT4fmU2bNkFLSwsmJiZKk+NEX9uhQ4ewYMECrF+/HosWLUL//v1RtmxZbNmyRaFsrVq1UK5cOWzatAknTpxAfHw8lixZguvXr6N9+/bQ0tJCbGwsxowZA0dHR/zyyy+FcEZERES5wxF/RERERERERFkoV64cKlSogMjISAQFBaF3797iPlmSr0WLFtDS0gKQMbpv4MCBCmUqVKiAcuXK5fi4nz59gpOTE27fvo3mzZujZs2aSpM5qvj7+2PJkiWwsbFBmzZt8OLFC5w7dw4TJ07E06dP8ccff8iVT0hIQMuWLXH58mXo6+ujbdu20NHRwfHjx3HkyBF07Ngxx8fOzqlTp9ChQwekpKSgZs2aaNSoEdLS0vDs2TMcOXIEaWlp6Nq1a7bttG/fHlKpFP7+/rCyskL79u3FfbKROgCQlJQEFxcXBAQEQCqVomXLljA0NERISAhWrFiBHTt2wN/fH3Xq1JFr//Lly2jfvj1iYmJgbW2Nbt26IS4uDkFBQQgJCYGfnx8OHjwovvdZcXBwgKWlJW7evInXr1/D0tJS3BcQECD3c9WqVRX2tW7dWq6958+fo3379rhx4wZMTU1Rv359GBgY4OrVq1i4cCF2796NoKAguc/cq1evMHDgQBgZGcHe3h41atRAQkICwsLCsGLFCvzzzz8ICQmBra1ttucDADNnzsSsWbNQvnx5HDlyRC5uoq/NxsYGc+fORYcOHcTP/e3btzF//nwcPnwY7u7uUFdXR//+/cU6WlpaWLFiBVxdXdGuXTtxe8mSJbF06VIAwJQpUxAdHY0TJ07IrTWYmJjIaYyJiKhIYuKPiIiIiIiIKBstW7ZEZGQkAgMDFRJ/FhYWqFatGiQSCUqUKKEw4k/Z+n45cenSJdSoUQP3799HiRIlch3z/PnzsWbNGgwbNkzcdvr0abRu3RorV67ExIkTUaZMGXHfjBkzcPnyZVStWhUBAQEoWbIkgIwE5MCBA+Hr65vrGFSZM2eO3OizzOLi4hSm3VNl6tSpaNiwIfz9/WFnZ6cyRk9PTwQEBMDGxgYBAQHiaMCUlBSMGDECGzduhJubG+7cuSMm8ZKSktCzZ0/ExMRg+PDhWL58OTQ1NQEADx8+hLOzM/z9/eHt7Y05c+ZkG6tEIoGzszN27NiBgIAA9OvXTzzOuXPnUK1aNYSHhyMgIACjR48W6ylL/AmCgF69euHGjRsYMmQIli5dKk5jmJqaiqlTp2Lx4sUYNGgQTp8+LdYzMjLCgQMHxJFMMikpKfD09MS8efMwZswYHDlyJMtzSU5OxuDBg7Ft2zbUq1cPhw8fhpWVVbbXgOhLyvzAhUyTJk1w6NAhjB49GitWrMC4cePQs2dPuc9/586dERYWhu3bt+PNmzeoXLkyBg8eDFNTU5w7dw7r16/H1KlT4eDggLS0NHh7e2P16tWIjo6GoaEhBgwYgIULFzIJSERERQan+iQiIiIiIiLKhixpl3kqwydPnuDhw4do3rw5JBIJgIyRf5GRkXj8+LFYTtn6fjm1cuXKPCX9AMDV1VUu6QcArVq1Qrt27ZCWliZ3Lh8/fsT69esBAEuXLhWTfgAglUqxatWqAr2pLZsyVdkoQiMjIzRs2LDAjvXp0yf8+eefADLOLfMUoJqamli+fDmsrKwQGRmJPXv2iPt2796Nx48fo1SpUvjjjz/EpB8AVKxYEYsWLQIArFixAp8+fcpRLLLk3cmTJ8Vt58+fx8ePH+Hm5oa6desiKCgIqampADISfKdPnxaThjL+/v44f/48atWqhTVr1sitXaahoQEfHx9Ur14dgYGBuHnzprjPwMAAXbp0URihqKmpiblz56JUqVI4fvw43r9/r/Ic3r17h7Zt22Lbtm3o0qULgoODs036JSUlIT4+Xu5F9DV5eXlBXV0db968waVLlxT2V6tWDXPmzMG6deswceJEmJqaIjk5GUOHDoWNjQ1mzpwJAJg0aRJmzZqFrl274uDBgxg2bBhWr16tNOlIRERUWJj4IyIiIiIiIsqGLGl39+5dvHjxAoD8NJ8yn6/z9/LlS0RERMi1kVOWlpZo1qxZnmPu3Lmz0u329vYAILeuXWhoKD58+ABzc3O0bdtWoY6FhQXatGmT51g+5+joCADo378/zp07Jya6voQrV67gw4cPMDU1VXpNdHV10adPHwDyiV3Ze9inTx9oa2sr1HN1dYWJiQnev3+P0NDQHMUiS/ydOnVK3CYb0demTRu0bt0a79+/FxMTN27cwKtXr1C9enW55JpsRF6PHj2goaE4mZOamhqaN28OAAgJCVHY/99//2HJkiUYNWoUBg8eDA8PD3h4eCA1NRXp6em4f/++0vgjIyPRuHFjBAcH45dffoGfn1+OEsLz5s2DkZGR+Cpbtmy2dYgKkqmpqTi9blRUVI7qzJs3D+Hh4Vi7di2kUinev3+PP//8E40bN8aGDRvQuXNn+Pj4oFevXti3bx/u3r37JU+BiIgox5j4IyIiIiIiIspG6dKlUalSJQD/Sw7JEkNOTk5iuc8Tf7L/VqpUCaVLl87VMTOPTMsLa2trpdsNDQ0BQG6UmuxGeFbHzG88mc2bNw916tTBsWPH0KxZMxgaGqJp06aYPn16jqf5zClZgrNChQoqy9jY2MiVzUk9iUQi7stcLyvW1taoVKkSnj59KiaEAwICYGhoiAYNGoiJQVkyUNX6fg8fPgSQMT2rRCJR+lq1ahUA4M2bN2K9hIQEuLq6olatWpgwYQJWrlyJTZs2YfPmzdi8eTNev34NACpH5A0dOhR37tzBjz/+iBUrVsitd5aVadOmIS4uTnw9ffo0R/WICkpaWhri4uIAQG6ErCoRERGYN28ePDw80KpVKwAZ6wUmJyejcePGcmWbNm0KAAgLCyvYoImIiPKIa/wRERERERER5UDLli1x7949BAYGol+/fggKCoKZmRmqV68ulqlatSosLCzE5GB+pvnU0dHJV7w5TcpkJpuyNLf7cqtEiRK4cuUKgoODERAQgPPnz+PSpUs4f/485s6di3nz5mHKlCkFdryipHXr1rh37x5OnjwJKysrhIaGwsXFBRoaGmjSpAl0dHRw8uRJcV1CWZ3M0tPTAWQkHGRJS1WqVasm/jxt2jT4+fnBzs4O8+fPR/369WFubi5O/dm4cWNcuHABgiAobWvAgAHYsmULtm3bBldXV3To0CFH56ytra101CTR13Lw4EEkJiZCIpGgXr16WZYVBAHDhg2DkZERFi9eLG6X/Q1MSEiQKy/7d0H+jSQiIsoPJv6IiIiIiIiIcqBly5ZYt24dAgMD8eTJE0RGRqJ79+4KN3ubN2+OvXv34tGjR+KIv7wk/r4m2WjER48eqSyT1b68kEgkcHJyEkdMfvr0Cb6+vhg5ciR+/fVXuLm5ZZvUygnZuUVGRqosIxtBl3lUpuxn2T5lZG3mZjRn69atsXr1agQEBKBUqVJIT08XE3va2tpo2rQpAgMD8fbtW5w5cwaamppy08kCEKfK7Nq1KyZOnJjjY+/atQsAsHPnTtSoUUNh/71797Ks7+7ujg4dOmDAgAHo1q0btm/fjh49euT4+ERfypMnT3DmzBm4ublBKpXK7du/fz9+/PFHABnTC2e3burGjRsRHByMbdu2wdTUVNxetWpVaGtrw8/PD7Nnz4apqSkSExOxdetWAEDt2rUL+KyIiIjyhlN9EhEREREREeWALEH14MED8UZv5mk+ZWRJmm3btolrPikrV5TUrVsXurq6ePPmjTjKLLPo6GicPHnyi8YglUoxfPhw1KhRA+np6bh+/XqO6slGq6laJ7BevXrQ19dHTEwMDh48qLD/48eP+OeffwDIJ2hl79nOnTvlpkWV8fPzw7t372BgYIC6devmKFYAaNWqFdTU1BAYGAh/f38AkFs/sXXr1khNTcX8+fORkJCAhg0bQk9PT64N2Ui73bt3qxydp0xMTAwAoFy5cgr7/P39ER0dnW0bvXr1gp+fH9TU1NC7d29s2bIlx8cnyqmrV6+iYcOG4ku2ruXatWvltsvWXI2JicHAgQNhYWGB5s2bo2/fvujWrRsqV66M7t27IyYmBi1btsTq1auzPO6rV68wefJktG/fHv369ZPbp6+vj/Hjx+Ply5eoVq0a3NzcUL16ddy8eRN9+/aFra3tl7kYREREucTEHxEREREREVEOlChRAvb29gAgTv+WVeJvyZIlAAB7e/tsR5gUNl1dXXFEzLhx4/Dq1StxX1JSEn755ReF6e3yY9GiRXjy5InC9jt37oijzpQlp5QpU6YMgIzRaikpKQr7pVIpRo4cCQCYMGECHj9+LO5LSUnBmDFj8PLlS1SoUAFubm7ivp49e8La2hrPnz/H+PHj5RKLkZGRmDBhAgBg1KhRCiOMsmJsbIy6desiPj4eW7ZsQZkyZWBnZyful43+W7lypdy/M+vatSvq16+Pf//9F4MGDZJbx0/m3bt3WLNmjVzcss/vihUr5MpGRERg+PDhOT6HTp064ejRo9DR0YGHh4e4niBRQYmPj8elS5fElywpHRUVJbc9KSkJQMYo2ClTpsDR0RGPHz/GoUOHcPToUXz48AEuLi7Yvn07AgICoK+vn+Vxx44di+TkZJUJwjlz5sDHxwd6eno4cOAAkpOTMWXKFPj6+hbo+RMREeUHE39ERF9AREQEVqxYAQ8PDzg4OEBDQwMSiQSzZ89WWUcikeTo9fkTtQkJCRg5ciSsrKwglUrRoEEDnD59WuVxDh8+DIlEgrlz5xbY+RIRERF9L2QjwmJiYmBqagoHBweFMg4ODjA1NRVHVxX1aT5l5syZg7p16+LmzZuwtbVF165d0bt3b1SsWBGnTp2Cu7s7gP+NsMuP2bNno1y5crC3t4erqyv69++Pli1bwsHBAQkJCfjhhx9Qp06dHLVlbW2NevXq4fXr13BwcMCAAQPw448/YurUqWIZb29vODs74/79+7C3t0enTp3Qp08f2NraYv369TAzM8Pu3bvlzk1bWxt79uyBqakpVq9eDVtbW/Tp0wedOnVC1apVERkZiXbt2sHT0zPX5y9L5n369ElutB+QMV2gmZmZOMpQWeJPTU0N+/fvR61atbB582ZUqFABTZo0Qd++fdGjRw/Url0bFhYWGDFihFziz9PTExKJBDNmzECNGjXQt29fODs7w8HBARUrVkTjxo1zfA4tW7ZEQEAAjI2NMXLkSCxYsCDX14FIFScnJwiCkO2rfPnyAAAzMzPMnz8fp06dwuPHj/HhwwckJyfj+fPnOHToEPr27ZujdU937NiBDx8+iO1+TiKRYNKkSbh//z5SUlIQFRWF+fPnF8jfRSIiooLCxB8R0RewevVqjB49Gps3b8bNmzeRlpaWbR13d3eVL9nNIolEorC+h7u7O1atWgVLS0u0a9cON27cQLt27XDx4kWFY3z48AE///wzHBwcMGnSpII5WSIiIqLvSOYkXvPmzRXW9wMyvrM1a9ZMaZ2iTF9fH0FBQfj1119haWmJ48eP48yZM3B2dkZoaCjU1dUBAObm5vk+1p9//olBgwZBQ0MDwcHB2Lt3LyIjI9GmTRv4+fnlevTM3r170a9fP8THx2Pnzp3YuHGjOH0nkJHEO378OFatWoWaNWvi7Nmz8PPzg6amJkaNGoX//vtP6XSd9evXR1hYGEaOHAl1dXX4+fnh7NmzqF27NlavXo3Dhw/n6YZ/5mTe54k9iUSCVq1aAQAMDQ3h6OiotI1SpUrh4sWLWLNmDRwdHREREYE9e/bg3LlzAIDhw4fD399fbjSiq6srgoOD4ezsjBcvXuDgwYN4/fo1vLy8cOzYMWhqaubqPBo0aICgoCBYWlpi6tSpmD59eq7qExEREVHBkwi5mQyeCsXJkyexfft2nD9/Hi9fvkRSUhJMTU1RvXp1dOzYEQMGDICFhQUAwNfXF4MGDYK7uzunGcil+Ph4GBkZoee8YJSp3ryww6Ei6NX9q9g+ri5CQ0Ozffp4w4YNiIiIQO3atVGnTh3MnTsXf//9N2bNmpWnzvDPP/+M1atXo02bNjhx4oS4PSwsDLVr14azszP8/f2hrq6OU6dOoXXr1ujUqRMOHz4s186YMWOwcuVKnD9/Hg0bNsx1HERE9O2QfbeJi4uDoaFhYYdDRN+4lJQUVK9eHXfv3s3R92GirMj+H7XHqTUaWFgVdjj0Fdx4F4OOAcf494OIiIqlotb/1ijsAEi16Oho9O3bV1xYvXz58mjZsiX09PTw8uVLhISEICAgADNnzkRAQAAaNGhQyBETkYxsfRSZnEwposqnT5+wY8cOAMCQIUPk9oWGhgIABg8eLD6B7ezsDFtbW4SEhMiVvXz5MlauXImff/6ZST8iIiIiUio0NBS1a9eW+/764cMHTJgwAXfv3kWNGjV4056IiIi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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n = 86 unique respondents\n"
          ]
        }
      ],
      "source": [
        "# Aggregate responses across all respondents for benefits\n",
        "response_counts_benefits = df_temp[benefits_list].sum()\n",
        "\n",
        "# Calculate percentages for the overall breakdown of benefits\n",
        "overall_percentages_benefits = response_counts_benefits / response_counts_benefits.sum() * 100\n",
        "\n",
        "# Sort percentages in ascending order for benefits\n",
        "sorted_percentages_benefits = overall_percentages_benefits.sort_values(ascending=True)\n",
        "\n",
        "# Map original column names to simplified labels for benefits\n",
        "label_map_benefits = {\n",
        "    'What do you see as the benefits of using an electric pressure cooker?/It would save time': 'Time savings',\n",
        "    'What do you see as the benefits of using an electric pressure cooker?/It is cleaner than alternatives': 'Cleaner',\n",
        "    'What do you see as the benefits of using an electric pressure cooker?/It would be cheap': 'Cheaper',\n",
        "    'What do you see as the benefits of using an electric pressure cooker?/It would be safer than alternatives': 'Safer',\n",
        "    'What do you see as the benefits of using an electric pressure cooker?/It would be convenient': 'Convenient',\n",
        "    'What do you see as the benefits of using an electric pressure cooker?/I dont see any benefits': 'No benefits',\n",
        "    'What do you see as the benefits of using an electric pressure cooker?/Other': 'Other'\n",
        "}\n",
        "\n",
        "# Apply the label mapping to the sorted percentages for benefits\n",
        "sorted_labels_benefits = sorted_percentages_benefits.index.map(label_map_benefits).str.capitalize()\n",
        "\n",
        "# Aggregate responses across all respondents for challenges\n",
        "response_counts_challenges = df_temp[challenges_list].sum()\n",
        "\n",
        "# Calculate percentages for the overall breakdown of challenges\n",
        "overall_percentages_challenges = response_counts_challenges / response_counts_challenges.sum() * 100\n",
        "\n",
        "# Sort percentages in ascending order for challenges\n",
        "sorted_percentages_challenges = overall_percentages_challenges.sort_values(ascending=True)\n",
        "\n",
        "# Map original column names to simplified labels for challenges\n",
        "label_map_challenges = {\n",
        "    'What concerns do you have about owning or using an electric pressure cooker?/It would heavily consume electricity': 'Consumes too much electricity',\n",
        "    'What concerns do you have about owning or using an electric pressure cooker?/Someone would not let me use it': 'Not allowed',\n",
        "    'What concerns do you have about owning or using an electric pressure cooker?/I worry for my familys health': 'Not healthy/safe',\n",
        "    'What concerns do you have about owning or using an electric pressure cooker?/The wiring is too weak': 'Wiring is too weak',\n",
        "    'What concerns do you have about owning or using an electric pressure cooker?/I am worried for the safety of the children in the household': 'Hazard to children',\n",
        "    'What concerns do you have about owning or using an electric pressure cooker?/Someone might steal it': 'Risk of theft',\n",
        "    'What concerns do you have about owning or using an electric pressure cooker?/I have no concerns': 'No concerns',\n",
        "    'What concerns do you have about owning or using an electric pressure cooker?/Other': 'Other',\n",
        "}\n",
        "\n",
        "# Apply the label mapping to the sorted percentages for challenges\n",
        "sorted_labels_challenges = sorted_percentages_challenges.index.map(label_map_challenges).str.capitalize()\n",
        "\n",
        "# Create subplots for benefits and challenges\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(18, 8))\n",
        "\n",
        "# Plot horizontal bar chart for benefits\n",
        "axes[0].barh(\n",
        "    sorted_labels_benefits,\n",
        "    sorted_percentages_benefits,\n",
        "    color='#71b3ff',\n",
        "    edgecolor='black'\n",
        ")\n",
        "axes[0].set_xlabel('Percentage of responses', fontsize=16)\n",
        "axes[0].set_ylabel('Benefits', fontsize=18)\n",
        "axes[0].set_title('Benefits of using an EPC', fontsize=18)\n",
        "axes[0].tick_params(axis='x', labelsize=16)\n",
        "axes[0].tick_params(axis='y', labelsize=16)\n",
        "axes[0].set_xlim(0, 70)\n",
        "axes[0].xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{int(x)}%'))\n",
        "\n",
        "# Add percentage labels to bars for benefits\n",
        "for i, value in enumerate(sorted_percentages_benefits):\n",
        "    axes[0].text(value + 1, i, f'{round(value)}%', va='center', fontsize=16)\n",
        "\n",
        "# Plot horizontal bar chart for challenges\n",
        "axes[1].barh(\n",
        "    sorted_labels_challenges,\n",
        "    sorted_percentages_challenges,\n",
        "    color='#e3555b',\n",
        "    edgecolor='black'\n",
        ")\n",
        "axes[1].set_xlabel('Percentage of responses', fontsize=16)\n",
        "axes[1].set_ylabel('Challenges', fontsize=18)\n",
        "axes[1].set_title('Challenges of using an EPC', fontsize=18)\n",
        "axes[1].tick_params(axis='x', labelsize=16)\n",
        "axes[1].tick_params(axis='y', labelsize=16)\n",
        "axes[1].set_xlim(0, 70)\n",
        "axes[1].xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{int(x)}%'))\n",
        "\n",
        "# Add percentage labels to bars for challenges\n",
        "for i, value in enumerate(sorted_percentages_challenges):\n",
        "    axes[1].text(value + 1, i, f'{round(value)}%', va='center', fontsize=16)\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "plt.savefig(fig_path + \"EPC benefits and challenges.png\", dpi=500, bbox_inches='tight')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "n_respondents = df_temp[benefits_list + challenges_list].any(axis=1).sum()\n",
        "print(f\"n = {n_respondents} unique respondents\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k1XThT8QitoG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "outputId": "d8b9fa4b-0644-484c-95b8-05c13d98a687"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Filter rows where Connection type is 2, 5, or 6\n",
        "filtered_df = df_temp[df_temp['Connection type'].isin([2, 5, 6])]\n",
        "\n",
        "# Aggregate responses by Connection type and benefit\n",
        "response_counts = (\n",
        "    filtered_df.groupby('Connection type')[benefits_list]\n",
        "    .sum()\n",
        ")\n",
        "\n",
        "# Calculate percentages within each connection type\n",
        "response_percentages = response_counts.div(response_counts.sum(axis=1), axis=0) * 100\n",
        "\n",
        "# Rename the connection types for better readability\n",
        "response_percentages.index = ['Individual metered', 'Collective metered', 'Collective unmetered']\n",
        "\n",
        "# Map new labels for the benefits\n",
        "response_percentages.columns = ['Time savings', 'Cleaner', 'Cheaper', 'Safer', 'Convenient', 'None', 'Other']\n",
        "\n",
        "# Transpose for horizontal bar chart\n",
        "response_percentages = response_percentages.T\n",
        "\n",
        "# Sort the data in descending order by the total percentage\n",
        "response_percentages['Total'] = response_percentages.sum(axis=1)\n",
        "response_percentages = response_percentages.sort_values(by='Total', ascending=True).drop(columns=['Total'])\n",
        "\n",
        "# Plot the horizontal clustered bar chart\n",
        "fig, ax = plt.subplots(figsize=(14, 8))\n",
        "response_percentages.plot(\n",
        "    kind='barh',\n",
        "    width=0.8,\n",
        "    color=['#71B3FE', '#D7CFD6', '#372E2A'],\n",
        "    edgecolor='grey',\n",
        "    ax=ax\n",
        ")\n",
        "\n",
        "# Customize the chart\n",
        "ax.set_xlabel('Percentage of responses (%)', fontsize=16)\n",
        "ax.set_ylabel('Benefits', fontsize=16)\n",
        "ax.set_xticks(range(0,51, 10))\n",
        "ax.set_xticklabels([f'{x}%' for x in range(0, 51, 10)], fontsize=14)\n",
        "ax.set_yticklabels(response_percentages.index, fontsize=14)\n",
        "ax.tick_params(axis='x', labelsize=14)\n",
        "ax.tick_params(axis='y', labelsize=14)\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "ax.spines['left'].set_linewidth(0.8)\n",
        "ax.spines['bottom'].set_linewidth(0.8)\n",
        "\n",
        "# Customize legend\n",
        "legend = ax.legend(\n",
        "    title='Connection type',\n",
        "    fontsize=14,\n",
        "    title_fontsize=16,\n",
        "    loc='lower right',\n",
        "    frameon=False\n",
        ")\n",
        "legend.set_title('Connection type')\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Fgb4ZTlGcCO"
      },
      "source": [
        "## Electric appliance ownership"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GTkYzajoqggz"
      },
      "outputs": [],
      "source": [
        "appliance_ownership_list = ['Do you own and use any of the following electric appliances to cook at least once a week?/Percolator',\n",
        "                            'Do you own and use any of the following electric appliances to cook at least once a week?/Hot plate',\n",
        "                            'Do you own and use any of the following electric appliances to cook at least once a week?/Cooking coils',\n",
        "                            'Do you own and use any of the following electric appliances to cook at least once a week?/Electric oven',\n",
        "                            'Do you own and use any of the following electric appliances to cook at least once a week?/Electric stove',\n",
        "                            'Do you own and use any of the following electric appliances to cook at least once a week?/Electric pressure cooker',\n",
        "                            'Do you own and use any of the following electric appliances to cook at least once a week?/Blender',\n",
        "                            'Do you own and use any of the following electric appliances to cook at least once a week?/Deep fryer',\n",
        "                            'Do you own and use any of the following electric appliances to cook at least once a week?/Rice cooker',\n",
        "                            'Do you own and use any of the following electric appliances to cook at least once a week?/Microwave',\n",
        "#                            'Do you own and use any of the following electric appliances to cook at least once a week?/Juicer',\n",
        "                            'Do you own and use any of the following electric appliances to cook at least once a week?/Popcorn machine',\n",
        "#                            'Do you own and use any of the following electric appliances to cook at least once a week?/Ice cream machine',\n",
        "                            'Do you own and use any of the following electric appliances to cook at least once a week?/Other',\n",
        "                            'Do you own and use any of the following electric appliances to cook at least once a week?/I dont use any of these appliances at least once a week']\n",
        "\n",
        "\n",
        "appliance_ownership_list_labels = ['Electric kettle',\n",
        "                                   'Hot plate',\n",
        "                                   'Cooking coils',\n",
        "                                   'Electric oven',\n",
        "                                   'Electric stove',\n",
        "                                   'Electric pressure cooker',\n",
        "                                   'Blender',\n",
        "                                   'Deep fryer',\n",
        "                                   'Rice cooker',\n",
        "                                   'Microwave',\n",
        "#                                   'Juicer',\n",
        "                                   'Popcorn machine',\n",
        "#                                   'Ice cream machine',\n",
        "                                   'Other',\n",
        "                                   'None'\n",
        "                                   ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TWThpAltweIz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bc57146e-961e-43d4-9284-7a29c79e9d13"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'Rectangle' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-517-e22fcfebd5d7>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;31m# Create a rectangle in figure coordinates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m fig.patches.append(Rectangle(\n\u001b[0m\u001b[1;32m     58\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;31m# lower-left corner\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'Rectangle' is not defined"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "respondent_count = len(df_temp)  # Total number of respondents\n",
        "appliance_percentages = (df_temp[appliance_ownership_list].sum() / respondent_count) * 100\n",
        "\n",
        "# Sort the percentages in ascending order\n",
        "appliance_percentages = appliance_percentages.sort_values()\n",
        "\n",
        "# Map sorted indices to appliance labels\n",
        "sorted_labels = [appliance_ownership_list_labels[appliance_ownership_list.index(col)] for col in appliance_percentages.index]\n",
        "\n",
        "# Create the horizontal bar chart\n",
        "fig, ax = plt.subplots(figsize=(10, 8))\n",
        "fig.patch.set_edgecolor('black')\n",
        "fig.patch.set_linewidth(1)\n",
        "\n",
        "# Plot bars with the correct fill color\n",
        "bars = ax.barh(\n",
        "    sorted_labels, appliance_percentages, color='#71b3ff', edgecolor='black', height=0.8\n",
        ")\n",
        "\n",
        "# Add data labels to the bars\n",
        "for bar in bars:\n",
        "    width = bar.get_width()\n",
        "    ax.text(\n",
        "        width + 1,  # Position to the right of the bar\n",
        "        bar.get_y() + bar.get_height() / 2,  # Center vertically\n",
        "        f\"{width:.0f}%\",  # Label format\n",
        "        va='center', fontsize=12, color='black'\n",
        "    )\n",
        "\n",
        "# Customize the plot\n",
        "ax.set_xlabel(\"Percentage of respondents\", fontsize=14)\n",
        "ax.set_ylabel(None)  # Remove the y-axis label\n",
        "ax.tick_params(axis='x', labelsize=12)\n",
        "ax.tick_params(axis='y', labelsize=12)\n",
        "\n",
        "# Remove the top and right spines\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "\n",
        "# Add a THIN black border around the plot area (axes)\n",
        "for spine in ['left', 'bottom']:\n",
        "    ax.spines[spine].set_color('black')\n",
        "    ax.spines[spine].set_linewidth(1)  # Thin black border\n",
        "\n",
        "# Format x-axis labels as percentages\n",
        "ax.set_xticks(range(0, 71, 10))\n",
        "ax.set_xticklabels([f\"{i}%\" for i in range(0, 71, 10)], fontsize=12)\n",
        "\n",
        "# Adjust layout for readability\n",
        "plt.tight_layout()\n",
        "\n",
        "# Add a thin black border around the plot area\n",
        "fig.canvas.draw()  # Make sure layout is finalized\n",
        "bbox = ax.get_position()  # Get position of the axis within the figure\n",
        "\n",
        "# Create a rectangle in figure coordinates\n",
        "fig.patches.append(Rectangle(\n",
        "    (bbox.x0, bbox.y0),  # lower-left corner\n",
        "    bbox.width,\n",
        "    bbox.height,\n",
        "    fill=False,\n",
        "    edgecolor='black',\n",
        "    linewidth=1,\n",
        "    transform=fig.transFigure,\n",
        "    zorder=1000\n",
        "))\n",
        "\n",
        "# Save the plot\n",
        "plt.savefig(fig_path + \"e-Cooking Appliance Ownership Rates.png\", dpi=500, bbox_inches='tight')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n",
        "print(f\"n = {len(df_temp)} unique respondents\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ThZvbz6eGx2L"
      },
      "source": [
        "# Appliance purchase habits"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xVGHglzEJkBl"
      },
      "source": [
        "## Appliance purchase new or secondhand"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xkNFzVqpwfTZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "outputId": "1b1deae8-796d-468a-c0d9-1e452e2eca35"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Aggregate responses for \"Do you know what an electric pressure cooker is?\"\n",
        "new_or_secondhand = df_temp['When the household buys an appliance like an iron or a percolator, do you normally purchase it new or secondhand?'].value_counts()\n",
        "\n",
        "# Calculate percentages for EPC knowledge\n",
        "new_or_secondhand_percentages = new_or_secondhand / new_or_secondhand.sum() * 100\n",
        "# Plot a hollow pie chart for EPC knowledge\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "wedges, texts, autotexts = ax.pie(\n",
        "    new_or_secondhand_percentages,\n",
        "    labels=new_or_secondhand_percentages.index,  # Use the response categories as labels\n",
        "    autopct=lambda p: f'{round(p)}%',  # Round data labels to the nearest integer\n",
        "    startangle=90,\n",
        "    wedgeprops={'width': 0.4, 'edgecolor': 'w'},\n",
        "    colors=['#e3555b', '#444ab4']  # Professional color palette\n",
        ")\n",
        "\n",
        "# Make labels the same size as the percentage data labels\n",
        "for text in texts:  # Category labels\n",
        "    text.set_fontsize(16)\n",
        "\n",
        "for autotext in autotexts:  # Percentage labels\n",
        "    autotext.set_fontsize(16)\n",
        "    autotext.set_position((autotext.get_position()[0] * 0.8, autotext.get_position()[1] * 0.8))  # Move inward\n",
        "\n",
        "# Add a circle in the center to make it a hollow pie\n",
        "center_circle = plt.Circle((0, 0), 0.6, color='white')\n",
        "ax.add_artist(center_circle)\n",
        "\n",
        "# Set aspect ratio to equal to ensure it's a circle\n",
        "ax.set_aspect('equal')\n",
        "\n",
        "# Adjust layout and show the plot\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig(fig_path + \"Appliance Purchase Habits.png\", dpi=500, bbox_inches='tight')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FCZ6UbIAJbW9"
      },
      "source": [
        "## Appliance purchase location"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zr5WEIH0GvLG"
      },
      "outputs": [],
      "source": [
        "appliance_purchase_location_options = ['Where does the household most often buy appliances from?/Local repairman',\n",
        "#                                       'Where does the household most often buy appliances from?/Department store',\n",
        "                                       'Where does the household most often buy appliances from?/Local market',\n",
        "                                       'Where does the household most often buy appliances from?/Kamyufu',\n",
        "                                       'Where does the household most often buy appliances from?/Appliance hawkers',\n",
        "#                                       'Where does the household most often buy appliances from?/Savings groups',\n",
        "                                       'Where does the household most often buy appliances from?/Other',\n",
        "#                                       'Where does the household most often buy appliances from?/I dont buy appliances',\n",
        "                                       'Where does the household most often buy appliances from?/Supermarket/showroom']\n",
        "\n",
        "\n",
        "appliance_purchase_location_options_labels = ['Local repairperson',\n",
        "#                                              'Department store',\n",
        "                                              'Local market',\n",
        "                                              'Local electrician',\n",
        "                                              'Appliance hawkers',\n",
        "#                                              'Savings groups',\n",
        "                                              'Other',\n",
        "#                                              'None',\n",
        "                                              'Supermarket/showroom']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K-lqNW9DG3yD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "12ee5218-8927-4d4e-e84a-dafb615a9aec"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n = 120 unique respondents\n"
          ]
        }
      ],
      "source": [
        "# Correct percentage calculation: What percent of respondents reported owning this appliance?\n",
        "respondent_count = len(df_temp)  # Total number of respondents\n",
        "appliance_purchase_locations = (df_temp[appliance_purchase_location_options].sum() / respondent_count) * 100\n",
        "\n",
        "# Sort the percentages in ascending order\n",
        "appliance_purchase_locations_percentages = appliance_purchase_locations.sort_values()\n",
        "\n",
        "# Map sorted indices to appliance labels\n",
        "sorted_labels = [appliance_purchase_location_options_labels[appliance_purchase_location_options.index(col)] for col in appliance_purchase_locations_percentages.index]\n",
        "\n",
        "# Plot the horizontal bar chart with data labels\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "bars = ax.barh(\n",
        "    sorted_labels, appliance_purchase_locations_percentages, color='#71b3ff', edgecolor='black', height=0.8\n",
        ")\n",
        "\n",
        "# Add data labels to the bars\n",
        "for bar in bars:\n",
        "    width = bar.get_width()\n",
        "    ax.text(\n",
        "        width + 1,  # Position to the right of the bar\n",
        "        bar.get_y() + bar.get_height() / 2,  # Center vertically\n",
        "        f\"{width:.0f}%\",  # Label format\n",
        "        va='center', fontsize=12, color='black'\n",
        "    )\n",
        "\n",
        "# Customize the plot\n",
        "ax.set_xlabel(\"Percentage of respondents\", fontsize=14)\n",
        "ax.set_ylabel(None)  # Remove the y-axis label\n",
        "ax.tick_params(axis='x', labelsize=12)\n",
        "ax.tick_params(axis='y', labelsize=12)\n",
        "\n",
        "# Turn all spines on and set a thin black border\n",
        "for spine in ax.spines.values():\n",
        "    spine.set_visible(True)\n",
        "    spine.set_color('black')\n",
        "    spine.set_linewidth(0.8)\n",
        "\n",
        "\n",
        "# Format x-axis labels as percentages\n",
        "ax.set_xticks(range(0, 101, 25))\n",
        "ax.set_xticklabels([f\"{i}%\" for i in range(0, 101, 25)], fontsize=12)\n",
        "\n",
        "# Adjust layout for readability\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig(fig_path + \"Appliance Purchase Locations.png\", dpi=500, bbox_inches='tight')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n",
        "print(f\"n = {len(df_temp)} unique respondents\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WaQpwQs-JfTF"
      },
      "source": [
        "## Appliance purchase priorities"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_temp = df_cooking_survey['Spotlight Kampala Cooking Su...']"
      ],
      "metadata": {
        "id": "O8otkOoLTXif"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XmSIzefnH8xl"
      },
      "outputs": [],
      "source": [
        "appliance_purchase_priorities_list = ['What do you prioritize when you decide where to buy an appliance?/Cheap',\n",
        "                                      'What do you prioritize when you decide where to buy an appliance?/Good quality',\n",
        "                                      'What do you prioritize when you decide where to buy an appliance?/I trust the seller',\n",
        "                                      'What do you prioritize when you decide where to buy an appliance?/Convenient to reach',\n",
        "                                      'What do you prioritize when you decide where to buy an appliance?/Good payment plans',\n",
        "                                      'What do you prioritize when you decide where to buy an appliance?/I always buy at the same place']\n",
        "\n",
        "\n",
        "appliance_purchase_priorities_list_labels = ['Cheap',\n",
        "                                             'Good quality',\n",
        "                                             'Trust the seller',\n",
        "                                             'Convenient to reach',\n",
        "                                             'Good payment plans',\n",
        "                                             'Always buys at same place']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qaAV9CAAHvfR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "f829360d-c086-4631-8504-63dc1fef0a25"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n = 120 unique respondents\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Correct percentage calculation: What percent of respondents reported owning this appliance?\n",
        "respondent_count = len(df_temp)  # Total number of respondents\n",
        "\n",
        "print(f\"n = {len(df_temp)} unique respondents\")\n",
        "\n",
        "appliance_purchase_priorities = (df_temp[appliance_purchase_priorities_list].sum() / respondent_count) * 100\n",
        "\n",
        "# Sort the percentages in ascending order\n",
        "appliance_purchase_priorities_percentages = appliance_purchase_priorities.sort_values()\n",
        "\n",
        "# Map sorted indices to appliance labels\n",
        "sorted_labels = [appliance_purchase_priorities_list_labels[appliance_purchase_priorities_list.index(col)] for col in appliance_purchase_priorities_percentages.index]\n",
        "\n",
        "# Plot the horizontal bar chart with data labels\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "bars = ax.barh(\n",
        "    sorted_labels, appliance_purchase_priorities_percentages, color='#71b3ff', edgecolor='black', height=0.8\n",
        ")\n",
        "\n",
        "# Add data labels to the bars\n",
        "for bar in bars:\n",
        "    width = bar.get_width()\n",
        "    ax.text(\n",
        "        width + 1,  # Position to the right of the bar\n",
        "        bar.get_y() + bar.get_height() / 2,  # Center vertically\n",
        "        f\"{width:.0f}%\",  # Label format\n",
        "        va='center', fontsize=12, color='black'\n",
        "    )\n",
        "\n",
        "# Customize the plot\n",
        "ax.set_xlabel(\"Percentage of respondents\", fontsize=14)\n",
        "ax.set_ylabel(None)  # Remove the y-axis label\n",
        "ax.tick_params(axis='x', labelsize=12)\n",
        "ax.tick_params(axis='y', labelsize=12)\n",
        "\n",
        "# Turn all spines on and set a thin black border\n",
        "for spine in ax.spines.values():\n",
        "    spine.set_visible(True)\n",
        "    spine.set_color('black')\n",
        "    spine.set_linewidth(0.8)\n",
        "\n",
        "# Format x-axis labels as percentages\n",
        "ax.set_xticks(range(0, 101, 25))\n",
        "ax.set_xticklabels([f\"{i}%\" for i in range(0, 101, 25)], fontsize=12)\n",
        "\n",
        "# Adjust layout for readability\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig(fig_path + \"Appliance Purchase Priorities.png\", dpi=500, bbox_inches='tight')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize a DataFrame to store the percentages\n",
        "heatmap_data = pd.DataFrame(\n",
        "    index=appliance_purchase_priorities_list_labels,\n",
        "    columns=appliance_purchase_location_options_labels\n",
        ")\n",
        "\n",
        "# Calculate the percentage for each combination\n",
        "for i, priority_col in enumerate(appliance_purchase_priorities_list):\n",
        "    for j, location_col in enumerate(appliance_purchase_location_options):\n",
        "        # Calculate the percentage of respondents who selected both the priority and the location\n",
        "        count = ((df_temp[priority_col] == 1) & (df_temp[location_col] == 1)).sum()\n",
        "        percentage = (count / len(df_temp)) * 100\n",
        "        heatmap_data.iloc[i, j] = percentage"
      ],
      "metadata": {
        "id": "HBBJERwHU7Gm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure numeric values\n",
        "heatmap_data = heatmap_data.astype(float)\n",
        "\n",
        "# Drop unwanted columns\n",
        "columns_to_drop = ['Other', 'Local electrician']\n",
        "heatmap_data_filtered = heatmap_data.drop(columns=columns_to_drop, errors='ignore')\n",
        "\n",
        "# Normalize to 100%\n",
        "heatmap_normalized = (heatmap_data_filtered / heatmap_data_filtered.to_numpy().sum()) * 100\n",
        "\n",
        "# Fix tick label casing\n",
        "def lowercase_second_word_full(label):\n",
        "    parts = label.split()\n",
        "    if len(parts) > 1:\n",
        "        parts[1] = parts[1].lower()\n",
        "        return ' '.join(parts)\n",
        "    return label\n",
        "\n",
        "heatmap_normalized.index = [lowercase_second_word_full(label) for label in heatmap_data_filtered.index]\n",
        "heatmap_normalized.columns = [lowercase_second_word_full(label) for label in heatmap_data_filtered.columns]\n",
        "\n",
        "# Define a stronger custom colormap with more contrast\n",
        "custom_cmap = LinearSegmentedColormap.from_list(\n",
        "    \"sharp_blue_red\",\n",
        "    ['#71b3ff', '#fff5f5', '#e3555b'],  # blue → white-ish → red\n",
        "    N=256\n",
        ")\n",
        "\n",
        "# Create figure and GridSpec with space for padding\n",
        "fig = plt.figure(figsize=(10, 6))\n",
        "gs = gridspec.GridSpec(1, 2, width_ratios=[1, 5], wspace=0.05)\n",
        "\n",
        "# Empty subplot (acts as left spacer)\n",
        "plt.subplot(gs[0])\n",
        "plt.axis('off')  # Hide axis\n",
        "\n",
        "# Plot the heatmap in the second column of the grid\n",
        "ax = plt.subplot(gs[1])\n",
        "sns.heatmap(\n",
        "    heatmap_normalized,\n",
        "    annot=heatmap_normalized.round(0).astype(int),\n",
        "    fmt=\"d\",\n",
        "    cmap=custom_cmap,\n",
        "    vmin=0,\n",
        "    vmax=25,\n",
        "    cbar_kws={'label': 'Share of all combinations (%)'},\n",
        "    ax=ax,\n",
        "    linewidths=0.2,               # Thin border width\n",
        "    linecolor='black'             # Border color\n",
        ")\n",
        "\n",
        "\n",
        "# Labels\n",
        "ax.set_xlabel(\"Purchase location\", fontsize=14)\n",
        "ax.set_ylabel(\"Purchase priority\", fontsize=14)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n",
        "ax.set_yticklabels(ax.get_yticklabels(), fontsize=12, rotation=0)\n",
        "\n",
        "# Format colorbar\n",
        "cbar = ax.collections[0].colorbar\n",
        "cbar.set_label('Share of all combinations (%)', fontsize=14)\n",
        "cbar.set_ticks(np.arange(0, 26, 5))\n",
        "cbar.ax.tick_params(labelsize=12)\n",
        "\n",
        "# Format colorbar border\n",
        "for spine in cbar.ax.spines.values():\n",
        "    spine.set_visible(True)\n",
        "    spine.set_edgecolor('black')\n",
        "    spine.set_linewidth(0.5)  # Adjust thickness as needed\n",
        "\n",
        "plt.savefig(fig_path + \"Appliance Purchase Priorities and Location Heatmap.png\", dpi=500, bbox_inches='tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Wb825W3bQXZf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "outputId": "f9835756-00c4-4b73-f48f-224fd0f303f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u2yqzkqiKbd2"
      },
      "source": [
        "## Financing strategies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "swQ5fEXuKe_v"
      },
      "outputs": [],
      "source": [
        "financing_strategies_list = ['How do you save money for the appliance?/Savings groups',\n",
        "                             'How do you save money for the appliance?/Personal savings']\n",
        "\n",
        "\n",
        "financing_strategies_list_labels = ['Savings group',\n",
        "                                    'Personal savings']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SpfwIozaJXlC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "outputId": "e135b42c-3987-43bd-e94d-efffb1d00786"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calculate the counts for the specified financing strategies\n",
        "financing_counts = df_temp[financing_strategies_list].sum()\n",
        "\n",
        "# Calculate percentages\n",
        "financing_percentages = (financing_counts / financing_counts.sum()) * 100\n",
        "\n",
        "# Map to custom labels\n",
        "financing_percentages.index = financing_strategies_list_labels\n",
        "\n",
        "# Plot a hollow pie chart for financing strategies\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "wedges, texts, autotexts = ax.pie(\n",
        "    financing_percentages,\n",
        "    labels=financing_percentages.index,  # Use the response categories as labels\n",
        "    autopct=lambda p: f'{round(p)}%',  # Round data labels to the nearest integer\n",
        "    startangle=90,\n",
        "    wedgeprops={'width': 0.4, 'edgecolor': 'w'},\n",
        "    colors=['#e3555b', '#444ab4']  # Professional color palette\n",
        ")\n",
        "\n",
        "# Make labels the same size as the percentage data labels\n",
        "for text in texts:  # Category labels\n",
        "    text.set_fontsize(16)\n",
        "\n",
        "for autotext in autotexts:  # Percentage labels\n",
        "    autotext.set_fontsize(16)\n",
        "    autotext.set_position((autotext.get_position()[0] * 0.8, autotext.get_position()[1] * 0.8))  # Move inward\n",
        "\n",
        "# Add a circle in the center to make it a hollow pie\n",
        "center_circle = plt.Circle((0, 0), 0.6, color='white', linewidth=0)\n",
        "ax.add_artist(center_circle)\n",
        "\n",
        "# Set aspect ratio to equal to ensure it's a circle\n",
        "ax.set_aspect('equal')\n",
        "\n",
        "# Adjust layout and show the plot\n",
        "plt.tight_layout()\n",
        "plt.savefig(fig_path + \"Appliance Purchase Financing.png\", dpi=500, bbox_inches='tight')\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oA0ekjY_ceR8"
      },
      "source": [
        "# Wiring capacity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mCtWZCSWapLt"
      },
      "outputs": [],
      "source": [
        "df_temp = df_wiring_inspection['Spotlight Kampala Wiring Ins...']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hBDPFAaiabks"
      },
      "outputs": [],
      "source": [
        "# Define the updated primary y-axis labels and secondary y-axis labels\n",
        "primary_y_labels = ['10 mm²', '6 mm²', '4 mm²', '2.5 mm²', '1.5 mm²', '< 1.5 mm²']\n",
        "secondary_y_labels = ['55 A', '40 A', '30 A', '20 A', '16 A', '10 A']\n",
        "\n",
        "# Correctly match the column names in the DataFrame, excluding \"Other\" and \"> 10 mm²\"\n",
        "filtered_columns = [\n",
        "    '6.3 What are the sizes [mm^2] of the visible conductors/wires?/10 mm^2 [8-gauge]',\n",
        "    '6.3 What are the sizes [mm^2] of the visible conductors/wires?/6 mm^2 [10-gauge]',\n",
        "    '6.3 What are the sizes [mm^2] of the visible conductors/wires?/4 mm^2 [12-gauge]',\n",
        "    '6.3 What are the sizes [mm^2] of the visible conductors/wires?/2.5 mm^2 [14-gauge]',\n",
        "    '6.3 What are the sizes [mm^2] of the visible conductors/wires?/1.5 mm^2 [16-gauge]',\n",
        "    '6.3 What are the sizes [mm^2] of the visible conductors/wires?/Less than 1.5mm^2'\n",
        "]\n",
        "\n",
        "# Recalculate counts and percentages for the specified labels\n",
        "counts = df_temp[filtered_columns].sum()\n",
        "percentages = (counts / counts.sum()) * 100\n",
        "\n",
        "# Replace 999 with NaN in the column for sharing connections\n",
        "df_temp['3.4 How many other households or businesses do you share the Yaka meter with?'] = df_temp[\n",
        "    '3.4 How many other households or businesses do you share the Yaka meter with?'\n",
        "].replace(999, pd.NA)\n",
        "\n",
        "# Include zeros for respondents who answered \"No\" in '3.3 Do you share the Yaka meter with any other households or businesses?'\n",
        "df_temp['3.3 Do you share the Yaka meter with any other households or businesses?'] = df_temp[\n",
        "    '3.3 Do you share the Yaka meter with any other households or businesses?'\n",
        "].replace({'No': 0, 'Yes': pd.NA})\n",
        "\n",
        "# Create a new column for averaging\n",
        "df_temp['Households for Averaging'] = df_temp['3.4 How many other households or businesses do you share the Yaka meter with?']\n",
        "df_temp.loc[df_temp['3.3 Do you share the Yaka meter with any other households or businesses?'] == 0, 'Households for Averaging'] = 0\n",
        "\n",
        "# Calculate the average number of households per connection for each wiring size\n",
        "households_per_connection = []\n",
        "for col in filtered_columns:\n",
        "    # Filter rows where the column is 1 and calculate the average for the \"Households for Averaging\" column\n",
        "    avg_households = df_temp.loc[\n",
        "        df_temp[col] == 1, 'Households for Averaging'\n",
        "    ].dropna().mean()\n",
        "    households_per_connection.append(avg_households)\n",
        "\n",
        "# Re-prepare box_data for households per connection\n",
        "box_data = []\n",
        "for col in filtered_columns:\n",
        "    # Filter rows where the column is 1 and collect data for the \"Households for Averaging\" column\n",
        "    data = df_temp.loc[df_temp[col] == 1, 'Households for Averaging'].dropna()\n",
        "    box_data.append(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BVDIxQSEtIxA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "ace71c20-869b-4477-f01a-290a581d2d01"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Update primary y-axis labels to include rated amperage\n",
        "primary_y_labels = ['10 mm² (55 A)', '6 mm² (40 A)', '4 mm² (30 A)', '2.5 mm² (20 A)', '1.5 mm² (16 A)', '< 1.5 mm² (10 A)']\n",
        "\n",
        "# Create subplots: one for percentages, one for households per connection\n",
        "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 6), gridspec_kw={'width_ratios': [1, 1]})\n",
        "\n",
        "# Plot the primary bar chart for percentages\n",
        "bars = ax1.barh(primary_y_labels, percentages, color='#4E79A7', edgecolor='black', alpha=1.0, zorder=3)  # Fully opaque bars\n",
        "ax1.set_xlabel('Percentage of responses', fontsize=14)\n",
        "ax1.set_ylabel('Wiring size (Amperage rating)', fontsize=14)\n",
        "ax1.tick_params(axis='x', labelsize=12)\n",
        "ax1.tick_params(axis='y', labelsize=12)\n",
        "ax1.set_xlim(0, 60)  # Limit x-axis to 60%\n",
        "ax1.set_xticks(range(0, 61, 10))\n",
        "ax1.set_xticklabels([f\"{x}%\" for x in range(0, 61, 10)])\n",
        "ax1.grid(axis='y', linestyle='-', linewidth=0.5, color='black', alpha=0.7, zorder=1)\n",
        "\n",
        "# Add integer data labels to the bars\n",
        "for bar, percent in zip(bars, percentages):\n",
        "    ax1.text(bar.get_width() + 1, bar.get_y() + bar.get_height() / 2,\n",
        "             f\"{int(round(percent))}%\", va='center', ha='left', fontsize=12, color='black', zorder=4)\n",
        "\n",
        "# Adjust y-tick positions to align perfectly between the subplots\n",
        "ax1.set_yticks(range(len(primary_y_labels)))\n",
        "ax1.set_yticklabels(primary_y_labels, fontsize=12)\n",
        "\n",
        "# Filter out box_data entries with fewer than 5 points\n",
        "filtered_box_data = [data if len(data) >= 3 else np.nan for data in box_data]\n",
        "\n",
        "# Replace np.nan with empty arrays to avoid crash on boxplot\n",
        "plot_ready_data = [d if isinstance(d, (list, np.ndarray, pd.Series)) else [] for d in filtered_box_data]\n",
        "\n",
        "# Plot the box-and-whiskers plot (omitting short categories)\n",
        "ax2.boxplot(plot_ready_data, vert=False, patch_artist=True,\n",
        "            widths=0.6,\n",
        "            boxprops=dict(facecolor='lightgray', color='black'),\n",
        "            whiskerprops=dict(color='black'),\n",
        "            capprops=dict(color='black'),\n",
        "            medianprops=dict(color='black', linewidth=1.5),\n",
        "            showfliers=False, zorder=3)\n",
        "\n",
        "ax2.set_yticks(range(len(primary_y_labels)))  # Align y-ticks with ax1\n",
        "ax2.set_yticklabels([''] * len(primary_y_labels))  # Remove y-tick labels\n",
        "ax2.set_xlabel('Households per connection', fontsize=14, color='black')\n",
        "ax2.tick_params(axis='x', labelsize=12, labelcolor='black')\n",
        "ax2.set_xlim(0, 10)  # Limit x-axis to 10\n",
        "ax2.set_xticks(range(0, 11))  # Ensure integer ticks\n",
        "ax2.grid(axis='y', linestyle='-', linewidth=0.5, color='black', alpha=0.7, zorder=1)\n",
        "\n",
        "# Align y-axis limits across both subplots for perfect alignment\n",
        "ax2.set_ylim(ax1.get_ylim())  # Match the vertical scale\n",
        "\n",
        "# Finalize layout\n",
        "fig.tight_layout()\n",
        "\n",
        "plt.savefig(fig_path + \"Wiring Capacity.png\", dpi=500)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print summary statistics for each wiring size category\n",
        "for label, data in zip(primary_y_labels, box_data):\n",
        "    if len(data) > 0:\n",
        "        print(f\"\\n{label}\")\n",
        "        print(f\"  Count:   {len(data)}\")\n",
        "        print(f\"  Mean:    {np.mean(data):.2f}\")\n",
        "        print(f\"  Median:  {np.median(data):.2f}\")\n",
        "        print(f\"  Min:     {np.min(data):.2f}\")\n",
        "        print(f\"  Q1:      {np.percentile(data, 25):.2f}\")\n",
        "        print(f\"  Q3:      {np.percentile(data, 75):.2f}\")\n",
        "        print(f\"  Max:     {np.max(data):.2f}\")\n",
        "    else:\n",
        "        print(f\"\\n{label}\")\n",
        "        print(\"  No data available.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "inRvs6tcEgnc",
        "outputId": "a5c01c30-dad2-444a-b819-19007b4c3b9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "10 mm² (55 A)\n",
            "  Count:   4\n",
            "  Mean:    2.25\n",
            "  Median:  2.00\n",
            "  Min:     0.00\n",
            "  Q1:      0.00\n",
            "  Q3:      4.25\n",
            "  Max:     5.00\n",
            "\n",
            "6 mm² (40 A)\n",
            "  Count:   2\n",
            "  Mean:    0.00\n",
            "  Median:  0.00\n",
            "  Min:     0.00\n",
            "  Q1:      0.00\n",
            "  Q3:      0.00\n",
            "  Max:     0.00\n",
            "\n",
            "4 mm² (30 A)\n",
            "  Count:   4\n",
            "  Mean:    1.50\n",
            "  Median:  1.00\n",
            "  Min:     0.00\n",
            "  Q1:      0.00\n",
            "  Q3:      2.50\n",
            "  Max:     4.00\n",
            "\n",
            "2.5 mm² (20 A)\n",
            "  Count:   32\n",
            "  Mean:    3.78\n",
            "  Median:  3.00\n",
            "  Min:     0.00\n",
            "  Q1:      1.00\n",
            "  Q3:      4.25\n",
            "  Max:     15.00\n",
            "\n",
            "1.5 mm² (16 A)\n",
            "  Count:   34\n",
            "  Mean:    3.65\n",
            "  Median:  3.00\n",
            "  Min:     0.00\n",
            "  Q1:      2.00\n",
            "  Q3:      4.00\n",
            "  Max:     13.00\n",
            "\n",
            "< 1.5 mm² (10 A)\n",
            "  Count:   4\n",
            "  Mean:    3.50\n",
            "  Median:  4.50\n",
            "  Min:     0.00\n",
            "  Q1:      3.00\n",
            "  Q3:      5.00\n",
            "  Max:     5.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m5c--tkSyIPn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "792032f2-5da1-43c3-ea0e-731257e62d91"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of unique users included: 73\n"
          ]
        }
      ],
      "source": [
        "# Calculate the number of unique users (rows) included in the calculation\n",
        "unique_users_count = df_temp.loc[df_temp[filtered_columns].sum(axis=1) > 0, '_id'].nunique()\n",
        "\n",
        "print(f\"Number of unique users included: {unique_users_count}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the number of respondents contributing to each wiring size bar\n",
        "for label, count in zip(primary_y_labels, counts.values):\n",
        "    print(f\"{label}: {int(count)} respondents\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_f-NbNADv_1",
        "outputId": "315cbf5d-a797-41c5-f27a-37bc18085499"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10 mm² (55 A): 4 respondents\n",
            "6 mm² (40 A): 2 respondents\n",
            "4 mm² (30 A): 8 respondents\n",
            "2.5 mm² (20 A): 52 respondents\n",
            "1.5 mm² (16 A): 57 respondents\n",
            "< 1.5 mm² (10 A): 5 respondents\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVXSh7nVwqA2"
      },
      "source": [
        "# Equipment damage"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YUZ0HBeh0mQG"
      },
      "outputs": [],
      "source": [
        "df_temp = df_wiring_inspection['Spotlight Kampala Wiring Ins...']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BlZDi0prydbP"
      },
      "outputs": [],
      "source": [
        "broken_appliance_list = ['2.6 Which equipment were damaged in the last month?/Bulbs',\n",
        "                                      '2.6 Which equipment were damaged in the last month?/Extension cable',\n",
        "                                      '2.6 Which equipment were damaged in the last month?/Coil',\n",
        "                                      '2.6 Which equipment were damaged in the last month?/Percolator',\n",
        "                                      '2.6 Which equipment were damaged in the last month?/Hotplate',\n",
        "                                      '2.6 Which equipment were damaged in the last month?/TV',\n",
        "                                      '2.6 Which equipment were damaged in the last month?/Phone charger',\n",
        "                                      '2.6 Which equipment were damaged in the last month?/Radio',\n",
        "                                      '2.6 Which equipment were damaged in the last month?/Flat iron',\n",
        "                                      '2.6 Which equipment were damaged in the last month?/Refrigerator',\n",
        "                                      '2.6 Which equipment were damaged in the last month?/Outlet/switch',\n",
        "                                      '2.6 Which equipment were damaged in the last month?/Voltage stabilizer']\n",
        "\n",
        "\n",
        "broken_appliance_list_labels = ['Lighbulb',\n",
        "                                'Extension cable',\n",
        "                                'Cooking coil',\n",
        "                                'Electric kettle',\n",
        "                                'Hot plate',\n",
        "                                'TV',\n",
        "                                'Phone charger',\n",
        "                                'Radio',\n",
        "                                'Flat iron',\n",
        "                                'Refrigerator',\n",
        "                                'Outlet/switch',\n",
        "                                'Voltage stabilizer']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0o03kQywxFqU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "outputId": "df415edb-4372-4dfd-aecc-421ff94d83d2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Correct percentage calculation: What percent of respondents reported owning this appliance?\n",
        "respondent_count = len(df_temp)  # Total number of respondents\n",
        "broken_appliances = (df_temp[broken_appliance_list].sum() / respondent_count) * 100\n",
        "\n",
        "# Filter out 0% values and sort the percentages in ascending order\n",
        "broken_appliances = broken_appliances[broken_appliances > 0].sort_values()\n",
        "\n",
        "# Map sorted indices to appliance labels using list indices\n",
        "sorted_labels = [broken_appliance_list_labels[broken_appliance_list.index(col)] for col in broken_appliances.index]\n",
        "\n",
        "# Plot the horizontal bar chart with data labels\n",
        "fig, ax = plt.subplots(figsize=(10, 8))\n",
        "bars = ax.barh(\n",
        "    sorted_labels, broken_appliances, color='#4E79A7', edgecolor='black', height=0.8\n",
        ")\n",
        "\n",
        "# Add data labels closer to the bars\n",
        "for bar in bars:\n",
        "    width = bar.get_width()\n",
        "    ax.text(\n",
        "        width - 1,  # Position slightly inside the bar for better alignment\n",
        "        bar.get_y() + bar.get_height() / 2,  # Center vertically\n",
        "        f\"{width:.0f}%\",  # Label format\n",
        "        va='center', ha='right', fontsize=12, color='white'  # Adjust alignment and color\n",
        "    )\n",
        "\n",
        "# Customize the plot\n",
        "ax.set_xlabel(\"Percentage of respondents\", fontsize=14)\n",
        "ax.set_ylabel(None)  # Remove the y-axis label\n",
        "ax.tick_params(axis='x', labelsize=12)\n",
        "ax.tick_params(axis='y', labelsize=12)\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "\n",
        "# Format x-axis labels as percentages\n",
        "ax.set_xticks(range(0, 16, 2))\n",
        "ax.set_xticklabels([f\"{i}%\" for i in range(0, 16, 2)], fontsize=12)\n",
        "\n",
        "# Adjust layout for readability\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DEGJiDmPU--s"
      },
      "source": [
        "# Safety"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d48VkNBQ2qen"
      },
      "outputs": [],
      "source": [
        "df_temp = df_wiring_inspection['Spotlight Kampala Wiring Ins...']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aoQHNvWz6mbO"
      },
      "source": [
        "## Injuries within past month"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QOUNkYzZ2oje",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825
        },
        "outputId": "7c99c3df-aa8f-46d5-c36b-a825407fcbd5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of valid responses included in the chart: 100\n"
          ]
        }
      ],
      "source": [
        "# Calculate the counts for 'Yes' and 'No' responses\n",
        "injury_counts = df_temp['2.7 In the last month, has there been anyone in this building that has been injured or electrocuted?'].value_counts()\n",
        "\n",
        "# Calculate percentages\n",
        "injury_percentages = (injury_counts / injury_counts.sum()) * 100\n",
        "\n",
        "# Plot a hollow pie chart\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "ax.pie(\n",
        "    injury_percentages,\n",
        "    labels=None,  # Remove all labels\n",
        "    autopct=None,\n",
        "    startangle=90,\n",
        "    wedgeprops={'width': 0.4, 'edgecolor': 'w'},\n",
        "    colors=['#71b3ff', '#e3555b']  # Professional color palette\n",
        ")\n",
        "\n",
        "# Add a circle in the center to make it a hollow pie\n",
        "center_circle = plt.Circle((0, 0), 0.6, color='white', linewidth=0)\n",
        "ax.add_artist(center_circle)\n",
        "\n",
        "# Set aspect ratio to equal to ensure it's a circle\n",
        "ax.set_aspect('equal')\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "fig.savefig(fig_path + \"Safety Incident Pie.png\", dpi=500)\n",
        "plt.show()\n",
        "\n",
        "n_responses = df_temp['2.7 In the last month, has there been anyone in this building that has been injured or electrocuted?'].notna().sum()\n",
        "print(f\"Number of valid responses included in the chart: {n_responses}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yF89lY3N6qbC"
      },
      "source": [
        "## Presence and condition of overcurrent protection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1__EmCoZ6txg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "outputId": "6a882554-90d3-4708-83f8-a3511212a7d2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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LlixxjOBardZS56uq6vjW69JCe7nS//HHHzsKTllycnJEdHS0MJlMJb65tI9Qz5kzp1y5r6X0R0RElPnNwpVu55dffnGMdufk5JQr05UsW7ZMABBDhw4tdd71lP6ybi89Pd1xm//+HRJCiB07djhK66Uq+txdrfSnp6cLf39/4efnJ86dO1fmZR566CEBQLz//vuO0y597/j666/LvN7V2H9Obdq0KfN8+4faPn36OE6zWCwiKCjosu9ZeXl5jm8+5s+fX+K8ixcvijp16ggA4qOPPhIfffSRAIq/rUhPT79i1v/85z8CKP72auXKlRV4tOTOOKffS9nndV46J9E+7/vmm29GTExMqeu0a9cOrVq1gqZpjjn0APDHH38AAIYPH17mfVWvXh0NGjRAamoqEhISAABt27aF0WjE559/jg8//BBJSUmV8riuxmQy4eabby51emxsLMLDw1FYWFiuucMdOnQAAMyePRtff/11uY4/KI/Ro0eXefq9994LANKWJLXf7913313m+ZfLXR4DBw4s8/QmTZoAQIl5q+vWrYMQAm3bti3zwMfmzZujZcuWFc7Sq1cvBAQElCvLpfbs2YO5c+fikUcewX333YcxY8ZgzJgxsNls0DQNx44dK3F5+/zjfx93kJqaij/++AO+vr4YNWqU4/Rdu3YhMTER9erVQ7t27crMYJ+7vHHjxnI9Vrvc3Fz88MMPmD59OsaPH+/IvmjRIgDAkSNHyrxerVq10KpVq1KnX+1nVRb7a8idd94Jk6n0+hIGgwHdu3cHUL7Hd7XXpKCgILRv3x42mw3btm0DACQnJ2P37t0wGAwYN25cubOXV+/evREaGnpN11m6dCkAYNSoUQgKCir39QoLC7F48WLMmDEDDz74IMaOHYsxY8bg008/BXD557SiBgwYUOq0iIgIREZGXvb8Bg0aAECp414q8tyVx6pVq2CxWBzHr5Tlan9Dd955Z7nvryz21/F/s79+rl+/HqqqAgC2b9+O3NxcRERElPkaGRAQgBEjRgAofmyXCgsLw8KFC+Hj44MnnngCTzzxBMxmMxYuXIiIiIjL5rNYLHj88ccBFB/3cNNNN13zYyT3xtV7vFRaWhoAlHgBsL9JX2kZz3r16mHPnj0l3tDta/5369btqvebmpqKhg0bol69enj77bcxdepUPPzww3j44YdRu3Zt3HDDDbj11lsxdOhQ+Pj4VOixXUnVqlUvuyxeSEgILl68iIKCgqvezo033oinn34ab775JkaPHg1FUdCgQQN07doVt912GwYOHAiD4do/U1/uZ28//dy5c9d8m5XBfr9Xy1cRtWrVKvP0kJAQACjxfNhzXGld9Li4OOzZs8fpWQAgLy8P99xzD37++ecr3m52dnaJfw8bNgyPPvoo/v77b5w7dw41atQAAHzzzTewWq0YPnw4wsPDHZe3/40dP378qgcPpqamXvH8Sy1evBhjx4694gfdf2e3u9rPqrCwsNw57I/v+eefx/PPP3/Fy5bn8dlv75577sE999xTrts7c+YMgOLXiGst5+VRkbX87QcNX8vKPps3b8bw4cMdj6csl3tOK+pyvwtBQUFIT08v8/zg4GAApX9PKvLclYf9dlesWFGhv6Ho6OgyBwSuxdVePy0WC9LT0xEdHV3u92Og7A/Y7du3x8yZM/Hss88CAGbNmoVOnTpdMd/hw4eRm5uLyMjIMgfHyPOx9HshIQR27doFAGjRosV13579W4MhQ4YgMDDwipe1j/wAwCOPPIJhw4bht99+w/r167F+/XosWLAACxYswMyZM7Fu3bpKX1ayIkX8ct544w08+OCDWLx4MdavX48NGzbgiy++wBdffIEOHTpg1apVV/15XCshxDVdvjJX6nCWijwnV3rTvp5dRa81y7Rp0/Dzzz+jcePGeOONN9ChQwdUqVLF8YHVvh/Gv5+3wMBADBs2DJ9//jm+/vprTJ8+HcD/Rv7t3wTY2Z/H2NhY9OvX74qZqlSpUq7s58+fx/Dhw2GxWPDUU0/hrrvuQlxcHIKCgmAwGPDXX3+hX79+l/2dq8y/Jfvji4+Pv+oSuc2aNSv37V3uW8tLuWplEn9/f6ffR35+Pm6//XakpKRg7NixmDhxIurXr4+QkBAYjUYcPXoUjRo1uubXkau52u/CtfyuOOu5s99u/fr10bVr1ytetqwPWa54/oBrf42/nIKCAvzwww+Of2/ZsuWq18nLywOAa/pWiTwLS78X+vPPP3Hx4kUAQN++fR2n27/yvNJuvfbzLv16tGbNmkhISMDTTz+N9u3bX1OWmJgYPPDAA3jggQcAFI803Hfffdi0aROeeeYZfPXVV9d0e64WFxeHRx55BI888ggAYNu2bbj77ruxbds2zJ49Gy+++OI13d7JkyfRunXrUqfblwu1jwjbmc1mWK1W5OTkOEbOLmUfKbxe1atXx/Hjx3Hq1KkyS9e/lzN1Fvvv3ZXuz1VZADiWtfz+++/LnFZkn85WlrFjx+Lzzz/Hl19+ienTp2Pnzp3Yu3cvatSogT59+pS4bM2aNQEUf2iu6FKk/7Z48WJYLBbccccdmDVr1jVlr2z2x3fbbbdhypQplXJ7hw8fxrhx4zBkyJByXcc+Gp2UlISsrCynjPZfK3sm+9KJV7N27VqkpKSgbdu2+Pzzz0ud78rntKIq8tyV93YBoFGjRpX2N3StTp48Webp9tcsPz8/x8CY/bXuctcByn4/tnv88cexe/du9OjRA+fOncNPP/2E9957D48++uhlby8mJgajR48u98ABeR7O6fcyWVlZjrW/+/TpU6Jg2uczLl26tMxtt3ft2uWY82qfXwvAsYvf5db1vhaNGzfG008/DQBlrjN8OfaR1bL2A3ClDh064KGHHgJwbfntLrdes/30f685bX+xL2td9L179+Ls2bPXnKEsPXr0AADMnz+/zPOvtKZ+ZerWrRsURcGOHTtw9OjRUucfPHiwwlN7KsJ+LEdZI47Lli1zTKMrS3x8PBo2bIiEhATHt0RA8fzef4+M2r9BOHjwIA4cOOD07EKIUuu/O5P9NeSHH36olJHOirwmxcbGOo5ZKqswl8XZrzv2KRbfffedYxT2SuzP6eWm23zzzTeVF85JKvp+crXnolevXvDx8cHq1atx4cKF6wtZQZf7+dtfP+Pj4x3HtLRv3x5BQUHIyMjAb7/9Vuo6FosFCxYsAIBSc++/++47fPrpp4iJicGCBQuwcOFC+Pr6YurUqdi+fftl8zVo0ABffvkl3nrrrQo9PnJ/LP1eQgiBJUuWOHbjrVq1KubNm1fiMvHx8ejUqRMsFgsmTJhQYgOUtLQ0TJgwAQAwYsQIx6gJAEydOhVhYWGYO3cu5syZg6KiolL3f/LkyRIveCtXrsSff/5ZagMrIQR+//13ANf21a19BLyyCtHV/Pzzz1i7dm2p6TNWq9Vx8F1Fpg18/PHHpQ7Wffvtt7F161YEBweXOsCwd+/eAIAXX3yxxNzYU6dOYfTo0ZX2VfEjjzwCo9GIhQsXlpq/vmDBAvzyyy+Vcj9XExcXh4EDB0LTNEycOBE5OTmO87KysjBx4sRKn7pwJfaDVt9///0Spx85cgQPPvjgVa9vn8bzySefOEq2fcOmS5nNZsycORNCCNxxxx1Yv359qcuoqoqVK1decdOvsrL/+OOPJQ6kV1UVM2bMuOYDgq/Hbbfdhg4dOmDr1q0YO3ZsmXOqL168iE8++aRcBXv8+PGoXbs2fvjhBzz99NMlfk/skpOTS70Gzpw5EwDw7LPPOg5kvtTBgwdLfMC2b+6UnJxcaQfzX2rQoEFo06YNEhMTMXTo0FLHXhQUFGDJkiWOf9uf0xUrVpTaaOqzzz7D999/X+kZK1tFnzv7e0BZG2wBxaPYjzzyCPLy8jBw4MAyd5ktLCzEb7/9Vu5vVq7Vjh07MHv27BKnrV+/Hh9++CEAlNiMz8/PD5MmTQIATJ48ucS3tlarFY899hiSk5NRp06dEt+IHDlyBOPHj4fBYMD8+fMRGxuLtm3bOt6bhw0bVuZGgwCwdetWNG7cmLtD65mMJYPIOexLIHbt2tWxMcmIESNE7969HZtx4Z/1zf+9RrHdpZtzRUdHiyFDhojbbrtNhISECODKm3NVqVLFcb2ePXuKu+66S9x6662ONZc7derkuPzbb78tgOKNuW688UYxatSoEhvxhIaGlmtzLrvff/9dABA+Pj7i1ltvFffdd58YN26c2LBhgxCifEvrlbU05+VOf+yxxwRQvDlKnz59xF133SUGDRokoqOjBQBRvXr1EsuaXo39uXn88ceFoiiie/fuYuTIkY7NioxGY6n18YUQ4sSJEyIsLMyxHNudd94punfvLvz9/UXv3r1Fly5dKmXJTiH+tySk/bkcNWqU6NChgwCKN3G63M/Xfp1/u9zP+2oZz58/L+Li4gQAERkZKQYPHizuuOMOERERIRo0aCAGDRpU5jJ2V2JfVnHmzJllnn+5pSEXLVrkWCK0RYsWYsSIEaJnz56Ozd8u9/O/9LEYjUbHz6h79+5XzDl16lTHZZs1ayZuu+02x6Zw9t+Djz/+uFyP2Wq1inbt2gmgeJOkW265RQwbNkzUrl1bmM1mx1K8/37MV9oQy+5yz/mVnD9/3rFkZmBgoOjSpYsYMWKEGDx4sGjdurXj53Tp3gZXyrJ//37H70lYWJjo3r27GDVqlLj99ttF06ZNhaIoIiYmptT1Xn31Vcdz2rhxYzF8+HAxaNAgxwZZ//59HDJkiAAgatasKUaOHCnGjRsnxo0b5zj/ar9bQlz59enUqVOiUaNGAv8sp9y3b18xcuRI0b179zI357Ivn+vj4yP69u0rRowYIRo3biwURRHPPvvsNf+dXsnVlkq+2t/45e6zIs9dYWGhY1nbNm3aiHvvvVeMGzeuxEZUVqtVjBo1SgDFS0+3adNG3HnnnWL48OGia9euIjAwUAAosUFXed47rubfm3M1a9ZMjBw5UvTo0cOxOddjjz1W6noFBQWOTSz9/f3FgAEDxPDhw0WtWrUcr3+XLuGbn5/veM8o6/fN/rt6xx13lJnz0j04SJ/4zOqI/QX20v8CAwNFtWrVRI8ePcTkyZPF1q1br3o76enpYtq0aaJJkybCz89PBAQEiDZt2og33nij1EZJl0pJSRHPP/+8aNu2rWOjoBo1aoguXbqImTNnlthd9NixY+KFF14QvXr1ErVq1RJ+fn4iPDxctGzZUjzzzDPXVJjt5s2bJ9q2bSsCAgIcj9/+Bl3ZpX/Xrl3imWeeEfHx8aJ69erCx8dHREVFiXbt2onXXnvtmjYWE6Lkm9/HH38sWrduLfz9/UVISIi4+eabHR9eynLw4EExePBgER4eLnx9fUWjRo3EK6+8IoqKiip1R14hijcvio+PF4GBgSIoKEh06dJF/Pjjj1f8+VZ26ReieLO4SZMmiRo1aggfHx9Rs2ZNMWnSJJGenu7YDG7ZsmVl3m5ZKlr6hRBi7dq1olevXqJKlSoiICBANG/eXLz66quisLDwqqVICCEGDBhQ6vf1SjZs2CDuuusuUbt2beHr6yuCg4NFw4YNxe233y7+85//lPmh/HJycnLE9OnTRaNGjYSfn5+Ijo4Wt99+u9i+fftlH7OzSr8QxSXnk08+ETfddJOIjIwUJpNJREdHi9atW4tJkyaVek6vliU7O1vMnj1b3HDDDSIsLEyYzWZRtWpV0aFDBzF16lSxcePGMq+3adMmMXLkSFG9enVhNptFRESEaNWqlXjqqafE6dOnS1w2PT1dTJgwQdSqVUuYzeZSj/16S78Qxc/TrFmzRIcOHURwcLDw9fUVtWvXFoMGDRILFiwocdmioiLx5ptvihYtWoiAgAAREREh+vbtK/76668K/Z1eibNKvxAVe+727dsnBg0aJKKiohxluqzfjT///FMMHjzY8fyGhYWJJk2aiBEjRohvv/22xF4dlVn6V61aJVasWCF69eolQkNDhb+/v2jfvr348ssvL3tdq9UqPvroI9G5c2fH+2q9evXEI488Umq/AfuGXT179iyxSaZdZmamqFu3rgAg3nnnnVLns/TrnyKEC78LJyJyoszMTNStWxdZWVlISUnhAWlERET/4Jx+IvI4W7duLXVaamoqRo8ejYsXL+LWW29l4SciIroER/qJyOMoioIaNWqgSZMmiIyMxPnz57Fr1y7k5uaiVq1aWL9+fYmDzYmIiLwdSz8ReZznn38eK1aswPHjx3Hx4kX4+PigXr16uPXWW/Hkk0+W2ASOiIiIWPqJiIiIiHSPc/qJiIiIiHSOpZ+IiIiISOdY+omIiIiIdI6ln4iIiIhI51j6iYiIiIh0jqWfiIiIiEjnWPqJiIiIiHSOpZ+IiIiISOdY+omIiIiIdI6ln4iIiIhI51j6iYiIiIh0jqWfiIiIiEjnWPqJiIiIiHSOpZ+IiIiISOdY+omIiIiIdI6ln4iIiIhI51j6iYiIiIh0jqWfiIiIiEjnWPqJiIiIiHSOpZ+IiIiISOdY+omIiIiIdI6ln4iIiIhI51j6iYiIiIh0jqWfiIiIiEjnWPqJiIiIiHSOpZ+IiIiISOdY+omIiIiIdI6ln4iIiIhI51j6iYiIiIh0jqWfiIiIiEjnWPqJiIiIiHSOpZ+IiIiISOdY+omIiIiIdI6ln4iIiIhI51j6iYiIiIh0jqWfiIiIiEjnWPqJiIiIiHSOpZ+IiIiISOdY+omIiIiIdI6ln4iIiIhI51j6iYiIiIh0jqWfiIiIiEjnWPqJiIiIiHSOpZ+IiIiISOdY+omIiIiIdI6ln4iIiIhI51j6iYiIiIh0jqWfiIiIiEjnWPqJiIiIiHSOpZ+IiIiISOdY+omIiIiIdI6ln4iIiIhI51j6iYiIiIh0jqWfiIiIiEjnWPqJiIiIiHSOpZ+IiIiISOdY+omIiIiIdI6ln4iIiIhI51j6iYiIiIh0jqWfiIiIiEjnWPqJiIiIiHSOpZ+IiIiISOdY+omIiIiIdI6ln4iIiIhI51j6iYiIiIh0jqWfiIiIiEjnWPqJiIiIiHSOpZ+IiIiISOdY+omIiIiIdI6ln4iIiIhI51j6iYiIiIh0jqWfiIiIiEjnWPqJiIiIiHSOpZ+IiIiISOdY+omIiIiIdI6ln4iIiIhI51j6iYiIiIh0ziQ7ABERVS4hBIQAhAAA8c9p/ztfgYABl5wApfhyigIoChQDx4OIiPSGpZ+IyI1omoCmFZd2RQEMBgUGg3LZyxcU2JCfb0Nevg05OVbk5BbBahWO2/nff/+77Zt7VoU1MRFFx45DUQyAQQGgAEYDFLMZiq8vDH6+UHz9oPj6QPH1heLjU3yejxmKqey3DlF8J8X/UBQoRqMTfkJERFQRLP1ERC6iaQKqKmA0lizyeflWpKYVICuzCNk5RcjLsyE3z4q8PBvy8qyO///v0/LzbY6OfS363hSLgl17kP3dwoo9EEUp/mAQGAhDcNA//wUX/29QEAwhwTAGBcEQEgJDaAiMISEwhASX+LDg+IDADwdERC7B0k9EVElUVUATAiajAkX5X6nPzinChQsWJCblIzklHxcuWJCcYkHKBQsuXLAg32KTmLoChIAoKIBaUAA1Pb1811GU4g8DkZEwRkbCVCXin/8fAWNUFExRkTCEhjqmFgkhAFUFDAZONyIiqgQs/URE18hq0xzFXgiBrKwiJKfkIzEpHyn/lPmUVAtSUvJxIdWCwsIKDMfrjRDQsrKhZWXDeuJk2ZdRFBjDw2GKjYGpaixM1arCVK0qzDVrwFgl8n8fCP75luBy04yIiKg0vmISEV2GfQ68yVRcNgsKbDhxKgcJCVk4fjIbJ05m49TpHJb6yiIE1IwMqBkZKDx4qOR5RiNM0dH/fBAo/kBgrlEd5lq1YPD3K766TQUMPBCZiKgsLP1ERABsNq3EQbNp6QU4mpCJ4yeyceJkDo6dyEJKiqXEKjjkQqoKW1ISbElJwI6SZxmjqsAcFwefuFow160Dn7p1YAwPA/DPtwJC8LgBIvJ6LP1E5HVsNg3Gf6bnWK0azpzNxdFjWcUF/0Q2TpzKRl6eh82z92JqahrU1DQUbNvuOM0QHARz7dowx9WCuU4cfOrXg7lqLABA2I8VUC6/KhIRkd6w9BOR7l1a8lPTLNi1Ow1792Xg8NFMnD2XB03j8L3eaDm5KNx/AIX7DzhOUwID4FO/PnwbN4RP40bwbVAPio8Pvw0gIq/A0k9EumOzaY55+BdSLdi5Kw1796dj774MpFywSE5Hsoi8fBTu2YvCPXuLTzAYYK5dCz4NG8C3cUP4NmkMY0R48WVtNh4oTES6wlc0IvJ4l5b8lAv5xSV/Xwb27E9HamqB5HTktjQN1pOnYD15CnnLlgMAjBER8GncEH4tmsOvTSsYI8IhNAEIjd8EEJFHY+knIo9zaclPTs7Hzt1p2LOveCQ/LZ0lnypOzciAZeNmWDZuBgCYYmPg26I5fFs2h1/L5jD4+xcfE6BwlSAi8iws/UTkEexFPzOzEBs3p2D33nTs3Z+O9PRC2dFIx2zJKbAlpyBv+QpAUWCuWwd+LZvDr1VL+DRqAMVo5FQgIvIIfJUiIrckhICmAUajgsSkPKxdn4QNm1Jw5Ggml80kOYSA9fgJWI+fQM7Pv0Hx9YVv86bwa98W/h07wBgcBKGqnAZERG6JpZ+I3IZ9FR2DQUHC8WysW5+EjZtTcOZsruRkRKWJwkIU7NiFgh27kPnZ5/CpXw9+HdohoFMHmKrGFq8KBHAaEBG5BZZ+IpJKVTUYjQaoqoa9+zOwbkMyNm1O4dx88ixCoCjhGIoSjiH72+9hqhrr+AbAp2H94stoPBiYiORh6Scil7PPzy8qUrFl2wWs35iMrdsvIDeXG2KRPtiSkpG7+E/kLv4ThuBg+LVvg4CuXeDbvKnjMvwGgIhciaWfiFzCXvRzcq1YvzEZGzclY+fuNBQVabKjETmVlpOD/FVrkb9qLQyhIfDv3AkB3bvCt0H94ilAisLdgYnI6Vj6ichp7HP0NSGwcXMKlvx1Fjt3pXEHXPJaWlY28pYtR96y5TBGVUFAl84I6B4Pc80aPAiYiJyKpZ+IKp19VP/M2Vz8seQMVq45j+xsq+xYRG5FTU1Dzq+/I+fX32GqXg0B8V0Q0D0epqgq/ABARJWOpZ+IKoX9gNy8PCuWrzyPpX+dxfET2bJjEXkE2/lEZH//I7K//xE+jRogsNdNCOjSGTCbASE4/5+IrhtLPxFVmH0tfYMB2LM3HX8uO4uNm1NgtXKePlFFFR1JQNGRBGR+8V8EdL0BgX16wqdOHEf/iei6sPQT0TWzT99JTSvAn0vP4K8V55CayiU2iSqTsFiQ9/dK5P29EubatRDY80YE3NgNBn9/fgAgomvG0k9E5aJpAooCWK0a1qxPwtK/zmLf/gzujkvkAtbTZ5D5xdfI/OY7+HfqgKDePeHbtDHLPxGVG0s/EV2Rfa7+ufN5+OmXk1i1NhH5+VxPn0gKqxWW9RthWb8RpqpVEdS/DwJ73giYTFz6k4iuiKWfiMpkn8Kzb38Gvl90Att3pMqORESXsCUlIfPzr5G14EcE9uyBoFv6wxQZwdF/IioTSz8RlaCqGhRFwZp1SfjhpxNcgYfIzYn8fOT+vgS5fy6DX/u2CL61P3wbN2L5J6ISWPqJCEIICAEUWTX8/sdp/PTbSR6YS+RpNA0FW7ejYOt2mGvXQtAtNyMgvgtgMHDJTyJi6SfyZpomYDAouJhZhB9/OoE/l51BXh7n6xN5OuvpM7j40WfI+mYBAvv2QvAt/aH4+3HeP5EXY+kn8kL2g3PPnM3F9z8cx+p1ibDZuAwPkd5o2dnI+fFn5C7+E4F9eiL49oEwBAdzwy8iL8TST+RF7Afn7t2Xge9/PI4du9JkRyIiFxCFhcXz/pf9jcAbuyH4jttgqhIJoWks/0RegqWfyAuoqgZAweq1ifjhpxM4cTJHdiQiksFqRd7ylchbuQYBXW9A8J23w1w1luWfyAuw9BPpmE3VYFAULF9xHt98l4CUCxbZkYjIHagq8teuR/66DfDv2B7BQ+6AT+1aXPGHSMdY+ol0yKZqMBoUrF2XhK/nJ+B8Yp7sSETkjoSAZcs2WLZsg1+b1gi9azjMtWpy5J9Ih1j6iXTEPmd/y9YL+PK/R3DqdK7sSETkIQp27UbB7j3w79wRoaOGwxQTzfJPpCMs/UQ6YF+NZ9eeNHzx9REkHOOGWkRUAULAsmkLLFu3I6BHN4QOHwJDWCgAcKlPIg/H0k/kwezr7Cccy8In/zmEAwcvyo5ERHqgqshfuRr56zYgqG9vhAy5HfD356g/kQdj6SfyQJomoChAUnI+5n1+CBs2pciORER6ZLUi948lyFu5GkG33IzgQbdCMZt4sC+RB2LpJ/IwmiaQnVOEL78+iiV/nYWmcVMtInIuYbEg58efkbdsOYIH346gm/sAAMs/kQdh6SfyEKqqwWoTWLDwGBb9fBIFharsSETkZbScXGR99Q3ylq9A6Jh74N+6JZf5JPIQLP1Ebk5VNRgMCv5cehZfzz+KzKwi2ZGIyMvZEpOQ/tps+LVuidD7RsMUEw2AB/sSuTOWfiI3JYSAoig4eSoHc9/byxV5iMjtFOzei4Innio+2HfEEMDHh6P+RG6KpZ/IDamqhsJCDfM+P4Q/l52BpslORER0GaqK3CXLkL9+A0KGDUFgn56AECz/RG6GpZ/IjdjX21+5OhGffX4ImZmcykNEnkHLyUXm/32J3L/+RtjYe+HXvCk39yJyIyz9RG5C0wQSk/Lxzvv7sHd/huw4REQVYjt7DmkvvQb/zh0Rdv8YGAKDoBhZ/IlkY+knksxm06BpAl99cxSLfjkJVeUSnETk+Sybt6Jg736EjhqGwD69AE3jlB8iiVj6iSSx76a7ZdsFfPjpAaSmFsiORERUqUR+PjL/8yXy16xH+MQHYKpejSv8EEnC0k8kgaYJpKUX4N0P92Prtguy4xAROVVRwjGkTJ2O4Fv7I2TYnYDBwFF/Ihdj6SdyIZuqAQJY8MNxLPjhGAoLuSwPEXkJVUXOr78jf9MWhD9wH/xateCBvkQuxNJP5AL2Nff378/Aux/ux7nzebIjERFJoV5IRdqrs+B/QyeEjRsDQ2AAR/2JXICln8jJbDYNqirw4acHsGTZWdlxiIjcgmXTFhTs3Y+w++5FYLeuHPUncjKWfiInEkLg2IlsvD57FxKT8mXHISJyKyIvDxff/xiWzVsR/uD9MARw1J/IWVj6iZxAVTUoioKv5x/FdwuPQ9O4DCcR0eUUbNuBlMNHEDZuDAK6dOaoP5ETsPQTVTJNE0hOseC12btwNCFLdhwiIo+g5eQi450Pikf9J9wP+Ply1J+oErH0E1US+7r7i/88jXmfH+LKPEREFWDZvBWFh48ifOID8G/TyrEQAhFdH5Z+okqgqhpycq2YNWcPtu9IlR2HiMijaZmZSH/9TQT2uhGhY+8FjEaO+hNdJ5Z+outgH93ftOUC3n5/L7KzrbIjERHpRt6K1SjYfxCRTz4Kc+1anOdPdB3410NUQTabhqIiFbPn7saLr+5g4ScicgI15QIuTJ+J3D+WAACExqmTRBXBkX6iChBC4MjRTLz+5m6kXLDIjkNEpG+qiqz/fofC/QcR8chDgL8fp/sQXSOO9BNdA1XVoKoa/vPFYTz59CYWfiIiFyrYtQfJk59B0ZEECMGlkImuBUs/UTmpqoa09EJMenwDFi46AX7DTETketrFi0h98VVkL1wEoWmc7kNUTiz9ROUghMCuPemY+Mg6HD+RLTsOEZF3EwI5i35B6ouvQcvOhlBV2YmI3B5LP9EV2HfSnb/gGJ6duRU5uTxYl4jIXRQdOoyUyc+gYM8+2VGI3B5LP9Fl2NTi1XlmvrwdX31zlNN5iIjckJaTi/RZc5C14AcIITjdh+gyWPqJyqCqAinJFjz02Hps3JwiOw4REV2JEMj56Vekv/EWRGEhp/sQlYGln6gMm7akYOJj63D2XJ7sKEREVE4Fu/Yg5annYEtKZvEn+heWfqJ/aJqAEAL/+fIwXnx1BywWvmEQEXkaNSUFF6bNgGXbDtlRiNwKSz8RipfjtFhsmPb8Vnz/w3HZcYiI6DqIwkJkzH0Pmf/9jst6Ev2DpZ+8nqoKnDmbiwcfWYcdu9JkxyEiokqSu/gPpL06G8JSwOk+5PVY+snrrVqbiEee3IDkFO6uS0SkN4X79iPlqWdhO5/I4k9ejaWfvJKqalBVgQ8+OYBZb+1GYSG/+iUi0is1NRUXnn0BBXv3QQghOw6RFCz95HVsNg0Wi4qp0zbj18WnZMchIiIXEIWFSH9jDnKXLpcdhUgKln7yKjZVw8XMQjzy5AbsO5AhOw4REbmSEMj64mtkfvE1D/Alr8PST15DVTWcPZuHhx/fgHPnuf4+EZG3yl3yF9LffBuw2TjPn7wGSz95BU0T2Lc/A49P2YiMi4Wy4xARkWQFO3bhwvMvQcvJZfEnr8DST7onhMCadUmYNmMr8i022XGIiMhNWE+ewoVnnufKPuQVWPpJ9xb9fBKvv7kLNhtXbCAiopLUjAxceO5FFOzdzzn+pGss/aRL9iXZPv3PQXz6f4fAFdqIiOhyREEB0mfNQf7a9VzSk3TLJDsAUWXTNAEhBGbN2Y1VaxJlxyEiIk+gabj48TxoObkIHjhAdhqiSsfST7qiqhqsVg0zXtqOXXvSZcchIiJPIgSy/vsttNxchI4cJjsNUaVi6SfdsNk05OZZ8cxzW3H8RLbsOERE5KFyfv4NWk4uwh4YCwBQFEVyIqLrxzn9pAuqquFCqgWPPLGBhZ+IiK5b3t8rkfHOBwA38SKdYOknj6eqAseOZ+ORJzcgOcUiOw4REemEZdMWpL3xFjfxIl1g6SePpmkCu/akYcozm5GdbZUdh4iIdKZwzz6kvvgqRGEhiz95NJZ+8liaJrBzdxpmvLQdBYV8ISYiIucoSjhevHtvXj6LP3ksln7ySJomsHtPceG3WjnXkoiInMt29hxSZ77M4k8ei6WfPI6qCezZl47nWfiJiMiFbOcTWfzJY7H0k0dRVYH9+zPw/IvbUFTEwk9ERK5lO5+I1BkvQcvLY/Enj8LSTx5DVQUOHMrAcy9sQ2EhCz8REclhS0xC6oyXWfzJo7D0k0dQVYFDhy/i2RnbeNAuERFJx+JPnoaln9yeqgocPpqJaTO2svATEZHbsCUmIfX5l6HlsviT+2PpJ7emqgJHEzIx7fktKCjgCyoREbkXW9I/I/4s/uTmWPrJbamqhoTjWXjmua2wWPhCSkRE7slR/PO5qg+5L5Z+ckuqquH4iRw8/ewW5FtssuMQERFdkS0pCWkvvQ5RZIXQuNgEuR+WfnI7qqrh5KkcPPXsZuTns/ATEZFnsJ4+g7TXZgOqyuJPboeln9yKqmo4dToXU6dvRl4eCz8REXmWoiNHkf7m24AQEELIjkPkwNJPbsOmajifmI+p0zYjN5eFn4iIPFPB7r3IeO8jAGDxJ7fB0k9uQVU15GRb8cxzW5CTa5Udh4iI6LpYNm1B5rwvoCiK7ChEAFj6yQ1omoDVJjBtxlakphXIjkNERFQp8v5eiaz538uOQQSApZ8ks3/t+eIr23H8RLbkNERERJUr59fFyPl1sewYRCz9JJeiKHj7/X3YvjNNdhQiIiKnyJr/PfJWruGKPiQVSz9J9c13CVj611nZMYiIiJzq4rzPUXjgIDfvImlY+kkKTRP4e+U5fPXNUdlRiIiInE9Vkf7Wu7AlJbP4kxQs/eRyqqph3/4MzHl3r+woRERELiMsFqS9OhtaXh6LP7kcSz+5lH0t/hkvb4fNxrWLiYjIu6jp6Uh7lbv2kuux9JPLXLoWf34+N98iIiLvZD15Culvvy87BnkZln5yCa7FT0RE9D8FO3Yh84v/yo5BXoSln5yOa/ETERGVlrdsOXL+WOJ4nyRyJpZ+cjquxU9ERFS2rK+/RcH2nRAq5/eTc7H0k9NxLX4iIqLLEAIZ730EWzKX8iTnYuknp1FVgfUbk7kWPxER0RWIwkKkvTEHoqiIK/qQ07D0k1OoqoYLqRa8OXeP7ChERERuT01JQfrc9wFFkR2FdIqlnyqdEAKqKjDjpW3It3BpTiIiovIo3LMX2d8tlB2DdIqlnyqdoiiY8+5enDqdKzsKERGRR8n5ZTHyN2/l/H6qdCz9VKk0TeC3309h5epE2VGIiIg80sUPP4UtiQf2UuVi6adKY1M1HDuehU/mHZIdhYiIyGOJwkKkzeKBvVS5WPqpUmiaQIFFxQuv7oDVxhcoIiKi66GmXOCBvVSpWPqpUigK8MobO5GaWiA7ChERkS4U7tmL7AU/cMdeqhQs/XTdhBD4ev5R7NjFHXeJiIgqU84vi1G47wDn99N1M8kOQJ5NVTXs3J2G+QuOyY5C5LWMRgWBgSYEBpgRFGRCUKAZgYFmBAaaEOBvgtGoQFEUGIwKDApgUBT4NmqA4NsHQqgaoBX/JzQVsKnQ8vOh5eRCy8mBlpsLLScXoqhI9sMk8k7/7NgbM/cNGIKCoBg4XksVowh+Z0QVpKoaMi4WYsLD65CTY5Udh0h3/HyNiI72R0y0P2Ji/vnfaH/ExgQgLMwXQYEm+PkZYTYbL3sbZb7ECwBXmCaslDGHWNhs0AoKoOXmQc3MhHYxE1q2/UNBDtT0DFgTk2BLTgGsfD0gqmy+zZqgyozpZf59EpUHSz9ViH0Drscmb8TRY1my4xB5rIAAE+rVCUGduGBUjQ1ATIw/atYIQnS0PwL8//dl7KUv1e7wpv/vtw57JiEE1Kxs2M6eg/XsOdiSkmBLLP5PzbgI8C2HqMJChg5G8JA73OI1gDwPSz9V2Nvv78WfS8/KjkHkMWJj/VGvTgjq1glBwwahaNQwFOFhfgDcr9RXBvtjcnwgsFphS0uH9eQp2JKSYT1zFkVHEqBmZMiMSeQ5FAVRM5+FT6MGUIyX/4aPqCws/XTNVFVgxerzeHPuHtlRiNxWTLQ/WraIQKMGYWjQIBT16gTD17d45P7fZdjb/Pvxq1lZKNx/EIVHjqLoSAKsp88UH2dARKUYwsMQO2cWlAB/zu+na8LST9dEVTUkJuVj4qPrUFjIN2Uiu9gYf7RqEYmWLSLQoV00wsN9dTl67yyXfhAQNhuKTpxE4b4DKDpyFIUJxyDy8iUnJHIfvq1aIurZp2THIA/D0k/XRFU1THp8A46fyJYdhUiqqrEBaNkiAq1aRKJDuyiEhfl6/Qh+ZRNCOH6W1uQUFO4/gMIDh1Cwew8/BJDXCxk1HMGDbuFoP5UbSz+VmxAC//fVEXz/w3HZUYhczmRS0LplJLp0jkV811iEs+S7nP1DgNA0FB1NgGXTVli274Cayj1CyAsZjYh+5QWY42pxfj+VC0s/lYvNpuHosSw8MXUjp9qS1/D3N6JDu2jEd4lBl84x8PU1lRh9Jnku/cBlTUyCZdMWWLbtgPXEScnJiFzHVLUqYt56DYrZLDsKeQCWfroqIQQKizQ88NAaJCdbZMchcqqwMB/c0DEG3btVRZtWkTAaDSz6HsD+HKk5ubBs2gzLtp0oPHAQsNlkRyNyqqD+fRE29l7ZMcgDsPRTucx5dy+W/sXlOUmfgoPN6HljdfS6sRoaNwpznM6i75nsHwC0wiIU7NyF/PWbULBzF6CqsqMRVT5FQdTM6fBp1JDTfOiKWPrpilRVw7YdqXj+xe2yoxBVKoMBaNsmCv371kTXG2JgMBQXfBZ9fXF8A5Cbh7y/VyJ/5eriXYOJdMQYGYmYt2dB8fXlaxhdFks/XZamCeTmWTHuwTXIzCySHYeoUlSrGoB+fWrilv61EBriw6k7XsT+XBcmHEPe0uXI37wVsFplxyKqFAE9uiFi0gTZMciNsfTTFb346g6s35gsOwbRdfHzNaJbfCxu7V8bTZuEs+h7uUun/+SvWo28FauLNwQj8nCRTz0JvzatOM2HysTST2VSVQ3rNiTj1Vm7ZEchqrCaNQIx+PY66NOrBnx9jCz7VIr9d6Lo9BnkLfsb+Rs2QVi4YAF5JkNICGLfeZO79VKZWPqpFPu0nvvGr0FWNqf1kOdp3iwcI4bWQ6cOMSz6VC6Ot0KbDfnrNiDn199hS+K3nOR5/Dq0R5Wpj8uOQW6IpZ/K9NJrO7BuA9/wyHMYDAriu8Rg1PAGqFc3hGWfKsz+tmjZsg05i37h1B/yOOGPTERAl86c5kMlsPRTCaqqYf3GZLzyBqf1kGfw8zOiX5+aGDm0HiIj/Vj2qdLYf5csu/ci54dFKErgbuTkGQzBwYh97y0o/pzmQ//D0k8OmiaQl2fD2PGrOa2H3F5YmA/uGFQHdwyqDT8/EwAut0nO4Vj15/ARZC/8CYX7D8iORHRVAd3jEfHwg7JjkBth6acSXn59B9au57Qecl/BwWYMH1IPg2+Lg8lkYNEnl3Ec9HvqNLK/X4SCHTtlRyK6oiozpsO3SSNO8yEALP30D5tNw5ZtF/DCKztkRyEqU0CACUPuqINhd9aFj4+RZZ+ksZd/a2ISshcugmXTFoBvpeSGjDExiJ37BhSzWXYUcgMs/QQAKCpSMWb8aqSmFsiOQlSCj48Btw+Mw10j6sPf38SyT27DUf5TLiDr869RsGu37EhEpQTfNhAho4bxtZNY+ql4Lv9X3xzFt98fkx2FyMFgAPr0rIH7xzZGaKgP37DIbdnLf8G+/cj8/GvYzifKjkT0P0YjYt58DaaqsZzm4+VY+r2cqgqkpllw34Q1sFo12XGIAAAdO0Rj4gNNUaN6IFfjIY8hhACEQO7S5cj+4SeIvDzZkYgAAD716yHq1Rf4WurlWPoJz76wDVu3XZAdgwjVqwXg0UnN0bZ1FMs+eSwhBERhIbLmL0De8pWAxgEVki9s7L0I7NebS3h6MZZ+L2azadixKw3PvbBNdhTycmazASOG1sOoEfVhNCgs+6QLQgjYUi4gc97nKNzHZT5JLsXfH7HvvglDSAiLv5di6fdiNpuG+yasQVJyvuwo5MXat4vCk4+0QJUqfiz7pDuODb527ELml99ATUmRHYm8mH+Xzoh8/GHZMUgSln4vpWkC335/DF99c1R2FPJSVSL98PDEpuh6Q1VO5SHdE0IAmobcP5Yie9EvEBaL7EjkpaJengGf+vV4UK8XYun3QpomkHGxEGMeWIXCQs41JdcyGhXcMSgO941uxM21yOsIIaBlZSPjw09QuGef7DjkhcxxtRE96xW+9nohln4v9eKrO7B+I3feJddq2iQcU59oierVAvmGQ17L/s1W3qo1yPxqPkQ+p1iSa4WNvw+BN/XgaL+XYen3MjZVw779GXhq+hbZUciLmEwK7r2rIUYMrQcALPxE+GfUPycXFz/8lBt7kUsZgoMR+8FcKH48lsqbsPR7GVUVGD9pLc6czZUdhbxE7VpBeG5aW9SuGcQ3F6J/cYz6r1mPzC++5qg/uUzQgH4IHX03X5e9CNds8iKaJrDolxMs/OQSigLcMSgOn7zfjYWf6DLsfxcB3bsi9p034dusieRE5C1yl/0NW1IyBPeR8Boc6fcSmiaQnV2E0fevRr7FJjsO6VyVSD9Me6o1WjaPlB2FyGPY345zf/8TWQt+BKxWyYlI73xbNkfUc8/IjkEuwpF+L2EwKPh43kEWfnK6Ht2q4ovPeqBFswjZUYg8iqIUb0wXdOsAxLz5Gsy1asqORDpXuHc/LNt3Qqiq7CjkAhzp9wI2m4YjCVl4fMpG2VFIxwIDTXj0oeboeWN1rrtPdJ2EEICqIuu/3yF3yTLZcUjHjDExiH1nNlfy8QIc6fcCJpMBH33KLeDJeerWCca8D7vjph7VAHB1HqLrpSgKFJMJYWPvQcSjD0Hx8ZEdiXRKTUlB3t8rOdrvBVj6dc5m07BpcwqOJmTJjkI6dVOPavjg7a6oUoVLvxE5g3/XGxD9xsswRkXJjkI6lf3jLwBLv+6x9OucyWTAF/89IjsG6ZDRqODBB5pg+lNtuLMukRMpigJT9WqIeet1+LZoLjsO6ZCWlYWcxX9yJR+dY+nXMZtNw5p1iTh5Kkd2FNKZsDAfzJ19AwbfVgcAp/MQOZuiKFD8fFHl2acQNOgW2XFIh3J++wPCUgAe6qlfLP06ZjAo+Hp+guwYpDONGoZi3kc90KRRGMs+kQspigLFYEDY3SMRMfkxKL6+siORjgiLBdmLfpYdg5yIpV+nbDYNK1ad50ZcVKn6962Jd9/qgtAQMws/kUT+HdsjevarMMbEyI5COpK77G9omVmc5qNTLP06ZTAo+O+3HOWnymEyKXj84eZ48rGWMBgUFn4iyRRFgSk2BjFvvgq/Nq1kxyG9sFqRtWAhFAProR7xWdUhVdWw9K+zSErOlx2FdMDf34jXXuqIATfXAsD5+0TuQlEUKL6+iHxmCoIH3yY7DulE/pr1sCYlc7Rfh1j6dUgI4JsFHOWn6xce7ov35nRF65aRLPtEbsi+i2/oiKEImzAO4N8pXS9NQ9b8BRzt1yE+ozqjqhp+X3IGqakFsqOQh6teLQAfvxuP2rWCWPiJPEBgzxsRMfkxwGSSHYU8XMHW7Sg6dZobdukMS7/OaBrw3ffHZMcgD9eoYSg+fCceERG+LPxEHkJRFPh3aIcqzz3NlX3oumX/8BMUo1F2DKpELP06oqoCvyw+hYyLhbKjkAfr0C4Kb8++AQEBJhZ+Ig+jKAp8mzRG1MszYAgKkh2HPFjB9p2wnjvPuf06wtKvIzabhu9/OC47Bnmwvr1r4JUXOnCHXSIPpigKzLVrIer1l2AID5cdhzyVEMj+8WfO7dcRPpM6oaoCP/58AlnZRbKjkIcaNbw+pj7RCorCFXqIPJ2iKDBFRyF61iswVY2VHYc8lGXTFthSUjjarxMs/TpRWKTix59PyI5BHmrcmEYYe28jACz8RHqhKAqMoSGIfv0lmONqy45DnkgIZP/4C0f7dYLPog5omsDCH48jN9cmOwp5oLH3NsKIofVlxyAiJ1AUBYq/P6JemQmfJo1kxyEPlL9+I2xp6Rzt1wGWfh0oKlLx82+nZMcgDzT67oYYNZyFn0jPFEWBYjYj6vlp8GvbWnYc8jSqipyffuUeEDrA0u/hVFXDH0vPID+fo/x0be4e2QB3j2wgOwYRuYCiKIDRiMipT8C3VQvZccjD5K1eCy0rC0II2VFKGDNmDBRFwRtvvFHi9F9++YVTVcvA0u/hDAYFvyw+JTsGeZhRw+tj9N0NZccgIhdSFAUwGFDlqSfh05Af+Oka2GzI+fk32SnK5Ofnh1mzZuHixYuyo7g9ln4PZlM1bNycguRki+wo5EFGDK3nOGiXiLyLoiiAyYQqzz8Dc+1asuOQB8lbsRoiP192jFJ69+6N2NhYvP7665e9zKJFi9CsWTP4+voiLi4Oc+bMcWFC98HS78FMRgMW/XxSdgzyIMPurItxYxrLjkFEEimKAsXHB1VeeA7GmBjZcchDiKIi5C77G0JVZUcpwWg04rXXXsP777+Pc+fOlTp/x44dGDZsGEaMGIF9+/bhhRdewPPPP48vv/zS9WElY+n3UJomcPxENvYdyJAdhTzEnbfXwQP3NZEdg4jcgKIoMAT4F+/cyw28qJxyly2XHaFMd9xxB1q3bo2ZM2eWOm/u3Lno1asXnn/+eTRs2BBjxozBww8/jDfffFNCUrlY+j2UwaDgx5+4Lj+VT6+bquPBB5rKjkFEbsS+jn/US8/DEBwkOw55AO1iJvI3bna70X4AmDVrFr766iscOnSoxOmHDh1C165dS5zWtWtXJCQkQHXDx+FMLP0eKjOzEKvXJcqOQR6gdctITH2ipdutukBE8tl37q3y/DQofn6y45AHyP19CRSjUXaMUrp3745+/fph2rRpsqO4LZZ+D6RpAj//dgo2G0scXVlc7SC8/EJ7GAwKly8jojIpigJz7VqoMn0qYDbLjkNuznryFAqPHHXL0f433ngDixcvxqZNmxynNWnSBBs2bChxuQ0bNqBhw4YwuuGHF2di6fdAqirw+5LTsmOQm4uM8MWbr3WGr4+RhZ+IrkhRFPg0aojIJx8FDKwGdGU5i/90y9H+Fi1a4K677sJ7773nOG3y5MlYsWIFXn75ZRw9ehRfffUVPvjgA0yZMkViUjn4l+1hbDYNy1ecQ3a2VXYUcmP+/kbMfq0TQkN9WPiJqFwURYFf29YIn/iA7Cjk5gq27YAtLd0tp42+9NJL0DTN8e+2bdti4cKFWLBgAZo3b44ZM2bgpZdewpgxY+SFlEQR7viM0RXdP3ENTp/JlR2D3JTRqOD1lzqidatIFn4iqpDM/36H3MV/yI5BbixoQD+E3nsXFH4z5DH4THkQVdWwc1cqCz9d0WMPN2fhJ6LrEnrXcPi2bC47BrmxvFVrIYo468CTsPR7EKPRgB+5GRddwajh9dG/by0WfiK6PoqCyCmPwxgTLTsJuSlhsSBv5Wq3PKCXysbS7yE0TeB8Yh6270yVHYXcVJfOMRh7byPZMYhIBxRFgeLriyrTpkLx9ZUdh9xU3vIVbnlAL5WNpd9DKArw408nwCMwqCzVqwVg2tTWbnlQFRF5JkVRYKoai/CHJ8iOQm7Kdj4RhYePcLTfQ7D0e4h8iw3LV56THYPckK+vAS/P7ABfXy7NSUSVS1EUBHTqiODbbpUdhdxU7rK/OdrvIVj6PYDNpmHFyvMoLNSufmHyOo8/3AI1qgey8BOR04SMGg7fVi1kxyA3ZNmyDVpenuwYVA4s/R7AZDLgrxUc5afSBtxcE7171mDhJyKn44G9VCabjQf0egiWfjcnhEBiYh6OHM2SHYXcTMP6oXj0IS6pR0TOpygKFB8fHthLZcpbsZpTfDwAS7+bEwJY8tdZ2THIzQQHm/HSzPYwGDjCT0Su4TiwdxIP7KWSbIlJKDyaAKFxGrI7Y+l3c4oCrFh1XnYMciOKAjz7dBtEhPtyWg8RuZSiKAjo3BGBN/eRHYXcTN7fq7g7r5vjs+PGVFXDnn3pSE0rkB2F3Mio4fXRtnUVFn4ikkIIgbB774KpalXZUciNWDZtgVZQKDsGXQFLvxszGg1YtpwH8NL/NG4UhnvvasjCT0TSKIoCGI2IePxhgCO79A9RWIj89Rt4QK8b41+rGyssVLF+Y7LsGOQm/HyNeO6ZtmDfJyLZFEWBOa4W1++nEvLXrOcBvW6Mpd9N2Wwa1q5PQkEBPzFTsfHjmiA6yo+j/ETkFhRFQcjwITDH1ZYdhdxE0dEEqBkXZcegy2Dpd1MmkwHLuTY//aNDuygMvKU2Cz8RuRdFQcQTjwBms+wk5A6EQN6adZzi46ZY+t1UekYBdu9Nlx2D3EBQkAnPTGkFIYTsKEREJSiKAlNsDEKH3Sk7CrmJ/PUbOcXHTbH0uyFV1fDX8nNgxyMAeGh8MwQH+3CUn4jckqIoCBp0C3waNZQdhdyA7ew5WM+f50CVG2Lpd0NGowHLV3JqDwGdOkSjT68aLPxE5PYiHn+Yu/USACB/9Xpw5NL9sPS7GU0TOJqQhbPn8mRHIcmCgkx46smWHC0hIrenKAqMEeEIvXeU7CjkBvI3bORGXW6Iz4gbWrb8rOwI5AYmjm/KaT1E5DEURUFQn17wa91SdhSSTE1LR+GRBAhNkx2FLsHS72Y0IbBqbaLsGCRZi2YR6NurJgs/EXkUIQTCH36Q03wI+WvXgxvLuBeWfjdiUzVs3XYBOTlW2VFIIoNBwROPtuC0HiLyOIqiwBAcjODbB8qOQpJZNm8BONLvVlj63YjJaMC6DdyB19sNvKU2alQP5Cg/EXkkRVEQfPtAGKOqyI5CEmk5uSjYd4BTfNwIS78b0TSBrdsvyI5BEoWF+uD+sY1kxyAiuj4GA0JH3y07BUlWsGUbp/i4EZZ+N6FpAoeOZCI7m1N7vNm4MY3h62PkKD8ReTRFURDQsT18mzWRHYUksmzfKTsCXYKl341s3MSpPd6scaMw3NyXB+8SkT4IIRA2fhzApRu9lpaVhaLjJ3iMmpvgX6KbMBgUbN7KqT3eymAAnuTBu0SkI4qiwBQbg8A+PWVHIYksm7dyoy43wdLvJlJS8nHmbK7sGCRJvz41UScuhKP8RKQriqIg9K4RMAQFyY5CkhRs28GNutwEnwU3YLNpWM+pPV4rOMiMB+9vwlF+ItIlxdcXIcMGy45BktiSkmFLYsdxByz9bsBkMmDzFk7t8VajRtSHv7+Jo/xEpEuKoiCwb2+YataQHYUkyd+8FUJVZcfweiz9biDfYsO+AxmyY5AEkZG+uH1gHAs/EemboiD8/jGyU5AkBdt2QDEaZcfweiz9ktl34VVVTu3wRqOGN4DRyMJPRPqmKAp8mzSGX8f2sqOQBEXHT0DNypYdw+ux9EtmMhqwcXOK7BgkQUy0P265mUt0EpF3EEIgdOQwbtbkjYSAZctWCBun+MjE0i+Zpgls28H5/N7o7pENYDDwzY+IvIOiKDBXrwa/9m1lRyEJCnbtgWLiFB+ZWPol0jSBA4cuIjfXJjsKuVj1aoHo27sGR/mJyKsIIRAyYqjsGCRB4YFDPJhXMpZ+ybgLr3cac08DfsNNRF5HURT41KwBv9YtZUchFxMFBSg6dpzLU0vE0i+RwaBgE5fq9Dq1awWhR7dqHOUnIq8khEDwiGGyY5AEBbv3ApomO4bXYumXKDEpD+cT82THIBcbc09D2RGIiKRRFAW+dePg26yJ7CjkYoV793HpTolY+iWx2TRs2MRVe7xNvbohiO9SlaP8ROTVhBAIGT5EdgxysaLjJ6FZLLJjeC2WfklMJgN27kqVHYNcbMTQepzPSEReT1EU+DZuBJ9GDWRHIVfSNBTs3c8DeiVh6ZdE0wQOHsqUHYNcKKqKH7rHc5SfiAj4Z27/0MGyY5CLFe7dBxhYP2XgT12SU6dzkG/hUp3e5LaBcVyxh4joH4qiwL9lC5jr1pEdhVyoYM8+Dn5JwtIvgc2mYc/edNkxyIX8fI247dY4vtAREV1CCIGQIXfIjkEupF5IhS0tTXYMr8TSL4HJZMD+gxmyY5AL9eldA76+/HMjIrqUoijwb98Wppo1ZEchFyrYtQfCxtkOrsYWIsn+AxdlRyAXURRg+JC6smMQEbklIQSC+veVHYNcqPDgYSgmk+wYXoelX4KUC/nIuFgoOwa5SIf20YiJDuDUHiKiMiiKgsAe3aD4+8uOQi5SdOSo7AheiaXfxTif3/sMHVyHy3QSEV2JyYSA7vGyU5CLqGnpUDOzZMfwOiz9LmY0KtjHqT1eI652MFq3rMJRfiKiqwi65WbZEciFCg8e4nr9LsbS72KKomD/AR7E6y0G3xbHUX4ioqtQFAXm2Bj4NGkkOwq5SNGRo+A61q7F0u9iOTlFOHc+T3YMcgF/fyN696zOUX4ionIQQiCob2/ZMchFCg8fhcJNulyKP20XUlWBPfs4yu8tunWtCpOJf2JEROWhKAr8O3eEIThIdhRyAevpMxBFRbJjeBU2EhdSFGAfp/Z4jQE315QdgYjIsxgMCIjvIjsFuYKmofDoMU6BdSGWfhcyGDif31tER/mjWZMITu0hIrpGgX17yY5ALlJ06DCgabJjeA2WfhcqLFRx7Hi27BjkAr17VufoBRE5xfz583HvvfeiVatWiI6OhtlsRmhoKDp27IjXX38dubm5ZV4vPT0d06ZNQ4sWLRAYGAgfHx/UqFEDQ4cOxdq1a8u8Tl5eHiZNmoSYmBj4+fmhU6dOWLly5WWz/f7771AUBa+99lqFHpuiKDBXrw5zXO0KXZ88S+GRo1CMRtkxvIYi2ExcQtME9u5Lx9TpW2RHIReY/2VPREdxoxkiqnzx8fHYuHEjmjRpgpo1ayIiIgIpKSnYtGkTLBYL6tevjzVr1qBatWqO6xw/fhzdu3dHYmIiIiMj0alTJwQEBODAgQM4dOgQAGDOnDl48sknS9zXkCFDsGjRIjRv3hx169bF8uXLYbVasW7dOnTu3LnEZXNzc9G0aVOEhYVhx44dMJvNFXp8QgjkLV2OzC++rtD1yXMo/v6o9uVn/FbcRTjS7yJCCOzdz6k93qBJ4zAWfiJymjlz5iAtLQ0HDhzA0qVL8e2332LFihU4e/Ys4uPjcezYMUyePLnEdZ588kkkJibilltuwenTp/HHH3/ghx9+wMGDB/Hpp58CAJ5++mmcO3fOcZ3du3dj0aJF6NWrF3bv3o1ff/0Vixcvhs1mwyuvvFIq17PPPovz58/js88+q3DhB4pH+wNu7AZcx22QZxAWC9TUNNkxvAZLv4sYjQYcOMhNubxBn541OLWHiJymU6dOiIiIKHV6ZGSkY1rNX3/9VeI8+5ScmTNnIjAwsMR548ePR4MGDWCz2bBt2zbH6Tt27AAA3HfffTD+MwWjV69eqF+/PjZu3FjiNrZt24YPPvgADz30UKlvACrC4O8P/7atr/t2yP0VHTvOTbpchKXfhY6f4Hx+vTObDejTi2vzE5EcJpMJAODr61vidD8/v3Jdv0qVKo7/n56eDgClPmBERkaWOG7AZrNh/PjxqFatWoXn8v+bEAJ+HdpVym2Re7OePCU7gtdg6XeR7JwiZGVzPVq969wxGn5+JtkxiMgL5eTk4IUXXgAADBo0qMR5/fv3BwC8+OKLyM/PL3HevHnzkJCQgBYtWuCGG25wnB4XFwcAjjn/AGC1WnHs2DFUr17dcdrcuXOxe/dufPjhhwgODq6Ux6IoCvw7dQB4kKfuFZ08xYN5XYTtxAWEEDh5kqP83uCmHtUghOBIPxE53V9//YVvv/0WmqY5DuTNycnBzTffjFmzZpW47JtvvomDBw/ijz/+QK1atdC5c2fHgbyHDx/GLbfcgnnz5jm+KQCAnj17Ijg4GHPmzEF8fDwaNGiAl19+Genp6bjrrrsAACdPnsSLL76IIUOGlPqgcb0Mvr7wbdIYhfsPVOrtknuxnjwtO4LXYOl3AZtN4PiJHNkxyMnMJgM6dYhm4Scilzh48CC++uqrEqeNGjUKc+fORWhoaInTY2JisHr1akycOBHffPMN/vjjD8d5NWvWRM+ePREVFVXiOlWqVMGsWbPw0EMPoWPHjo7TmzRp4vhGYeLEiTCbzXjvvfdKXDc/Px8BAQHX9fiKp/i0ZenXOS0nB2pmJoxhYbKj6B6n97iAyaTg1GmWfr1r2SICPj78ipKIXOPxxx+HEAJFRUU4duwY5syZgyVLlqBp06al1t0/fPgw2rRpg8WLF+Ojjz7C2bNnkZWVhdWrVyMmJgaTJ0/GgAEDoP7rgMqJEydi06ZNmDJlCsaPH48PP/wQ27dvR3h4OObPn49ly5Zh9uzZqFq1KvLz8zFp0iSEhoYiMDAQUVFRmDFjRqnbLC9FURDQ9YarX5A8XtHxkxDcpMvpuE6/izz65AYcOpIpOwY50cMPNsOgW2tzpJ+IpNmyZQtuuOEG1KhRA0eOHIG/vz9sNhtatWqFgwcPYuHChRg6dGiJ61y8eBFNmzZFcnIyPv/8c4wdO/aq95ORkYHGjRujcePGWLNmDRRFweDBg/Hrr79iypQpiI+Pxy+//ILPP/8cTz75JObMmVPhx5Ty9HM82FPnQoYORvDg2zi338k40u8ip85wpF/vunerysJPRFJ16tQJTZs2xdmzZ7F9+3YAxR8EDh48CF9fXwwePLjUdcLDwx0H+v7999/lup/JkycjOzsbn31WvLHSkSNH8PPPP2PEiBGYNWsWBg4ciP/7v/9Dp06d8MEHH1x2l+CrEULAn6v46B4P5nUNln4XSE2zwGLhGrR6VicuGOFhvle/IBGRk9nX4b9w4QIA4MyZMwCAgIAAx3r7/2Y/BiAj4+qbSK5atQpffvklpk2bhsaNGwMo3sgLALp06VLisvHx8SgqKsLBgwev/YHgn1V8OMVH93gwr2uw9DuZpgmuz+8FbugUww25iEi6tLQ07NmzBwDQsGFDAHAsr3nx4kUkJCSUeb0tW7YAAOrUqXPF2y8sLMSDDz6IJk2aYNq0aY7T7d9y5uXllbi8/d/X8y2ouWosjNFRV78geSw1PR1avkV2DN1j6XcyTRM4eYpTe/Sue3ys7AhE5AUOHjyI+fPno6CgoNR5R48exdChQ1FYWIjOnTujRYsWAIAbbrjBUfzvv/9+pKamOq6jaRreeOMNbNq0CQAwcuTIK97/yy+/jISEBMybNw8+Pj6O09u2bQsAmD9/PiyW4vKWlpaGn376CX5+fmjatGmFHzOn+HgHW2Ki7Ai6xyU7ncxkMnDlHp0LC/NB3TohnM9PRE534cIF3H333ZgwYQLatGmDGjVqoKioCGfOnMHOnTuhaRqaNGmC77//3nEds9mMr7/+GgMHDsTatWtRv359dOrUCcHBwdizZw+OHz8OAJg+fTq6det22fs+cOAAZs+ejQkTJqBr164lzqtfvz5GjhyJ7777Ds2aNUPbtm2xYcMGXLhwAdOmTXNMOaoo/xs6IfePpdd1G+TerGfPw1wnjnP7nYir97jA+ElrOdqvY/361MDkx1qy9BOR06WmpmLevHlYt24dDh8+jNTUVFitVkRERKBFixYYPHgwxo4dC1/f0scYnThxAnPnzsWKFStw+vRp2Gw2REVFoVOnTpg4cSL69Olz2fsVQiA+Ph6nTp3CwYMHS+0DAABFRUWYMWMG5s+fj5SUFNSsWRMPPvggpkyZct2vj0LTkPTAQ9ByKnZAMLm/4NsGImTEEJZ+J2LpdzJVExh4x1JYbVx/Vq9eeK4dunSOYeknInKi9Hc/hGXDJtkxyEn8OrRDlalPyI6ha5zT72TJyfks/DqmKECbVpEs/ERETiSEgG/jhrJjkBPZEpNkR9A9ln4nUlUNx45nyY5BTlSzRhACAsyyYxAR6ZqiKPBt3kx2DHIiW3IKd+V1MpZ+JxICnMuvc82bhXOpTiIiFzBVqwrF3192DHIWVYWali47ha6x9DsRV+7Rv+ZNI2RHICLyCoqiwKdhfdkxyImsZ89xtN+JWPqd7PQZrjSgZ+3aVuF8fiIiFxBCwLcR5/Xrme18IsDS7zQs/U6WcoE7zOlVlUg/RIT7yY5BROQVFEWBT4vmsmOQE1nPJwJcstNpWPqdKDu7CFYrP7HqVfNm4bIjEBF5Fd96dVgKdcyWnMxvz52Ipd+JOMqvb82bRvAgXiIiF1JMJphr15Idg5yEB/I6F0u/k6iqQGJSnuwY5EScz09E5Fqc169vasZFDqY5EUu/k2ia4Ei/jgUGmlC9WqDsGEREXseHm3Tpl6pCy8qWnUK3WPqdxGhUcIGlX7caNQjjKD8RkYtxky79U9PSZEfQLZZ+JzEYFI7061hcXDC/giQiksAYHARjVJTsGOQktpQLXKvfSVj6nYilX7/iagfLjkBE5LV86teVHYGcRE3P4Fr9TsLS70QXUln69apRg1BO7yEikkAIAVO1qrJjkJPY0tIAA+upM/Cn6iRFRSry8myyY5ATKApQswYP4iUiksXM0q9balo6FJZ+p+BP1UkuZhbJjkBOEhPjD7OZm8MQEcmgKApMdeNkxyAn4Vr9zsPS7yRpaZzao1d1OJ+fiEgqU3S07AjkJGo6S7+zsPQ7gaoKXEgtkB2DnCSuNlfuISKSyWA2wxAeLjsGOYGWkwth4/RoZ2DpdwJNE8i4yNKvV1y5h4hIPnP1arIjkJNouXmyI+gSS78TGAxAenqh7BjkJA25cg8RkVRcwUfftJwc2RF0iaXfCYxGA9IzONKvR0ajgtiYANkxiIi8nqk6S79eqZlZsiPoEku/k2RkcKRfj2Ki/WEy8c+GiEgmRVFgrllTdgxyEi07G0JVZcfQHbYXJ8m4yNKvRzHR/rIjEBERAHPNGrIjkJNo2TkAF8yodCz9TpKXb5UdgZwgmqWfiMgtGENDoPj4yI5BTqByTr9TsPQ7iSWfX0vpUUy0P5frJCJyE6aqsbIjkBNoOTnFq6JQpeJP1EkKCrnGrB5xeg8RkfswxcbIjkBOoGXnQGHpr3T8iTpBUZEKTZOdgpwhNjaAy3USEbkBIQQMISGyY5ATaDm5siPoEku/ExQUcmqPXsVEcblOIiJ3YQjhZol6pGVny46gSyz9TlBgYenXq9Aws+wIRET0D5Z+feKOvM7B0u8E+QWcz69Hfn5G+PmaZMcgIqJ/GINZ+vVIK+QGp87A0u8E+Xks/XoUGeErOwIREf1DURQYIsJlxyAnEIVFsiPoEku/E3CNfn2KjPCTHYGIiC5hDGfp1yWbDYIrolQ6lv5KpmkC+fkc6dej4GDO5ycicieGoCDZEchJhJUDqJWNpb+SaZqAhQfy6pK/P+fzExG5E4M/v4HVK07xqXws/ZVMCMBi4Ui/Hvn7m7gbLxGRG1FMJig+PrJjkBOIIpb+ysbSX9kUwFLAkX49CvA3yo5ARET/YuAKProkCgtlR9Adlv5KpgDI50i/Lvn5cXoPEZG74Vr9+iQKuGxnZWPpr2QGg4ICln5dCuCcfiIit8PSr09aAUf6KxtLfyUzGBSO9OuUP6f3EBG5HU7v0SdhsfA4ukrG0u8EXL1Hn7h6DxGR+1FMfG3WI1FUVLw6ClUaln4nKCjgSL8eBQSYoCiK7BhERHQpvizrEwt/pWPpdwL+nupTCDfnIiJyQ2z9usVCValY+p3AaOQLkB4FBPArZCIit8NvYPWJhb/SsfQ7gcHAFyA94vNKROR+2Pl1iqW/0rH0OwHLoT5pGl+AiIjcSfHqLnzP1SOu3FP5WPqdgKVfn/gCRETkhjjUr098z610LP1OwNKvT6rKFyAiIrfDt1yicmHpdwIeyKtPLP1ERO6I77m6xJH+SsfS7wQc6dcnlXP6iYjcD6f36BPfcisdS78TGPhT1SWO9BMRuSF2fp0SLP6VjPXUCTjSr08aSz8RkRviey5RebD0OwFLvz5ZbZrsCERE9G98y9UlxWzmc1vJWPormRCCB/LqlNXKkX4iIrdjU2UnICdQzGbOl65k/GlWMiEAI0f6dUlVNa7VT0TkRhRFgZaTIzsGOYHi6wOFB2lXKpZ+J+D0Hn3Kz7fJjkBERP+i5ebJjkBOoPj4yo6gOyz9lUwIln69ysmxyo5ARET/ouXmyo5ATqD4svRXNpb+SidY+nUqO6dIdgQiIvoXLY8j/Xqk+JhlR9Adlv5KJgRg4IG8usSRfiIi98PpPfqk+PjIjqA7LP1OwAN59Sk7x8qDioiI3IjQNIiCAtkxyAk40l/5WPormRBAQIBJdgxyguxsTu8hInInWmGh7AjkJIqZpb+ysfRXMkUBQoL5i6pHnN5DROReRH6+7AjkLCz9lY6lv5IZDAqCgzkPTY94IC8RkXvRsrlGv14pJs6aqGws/ZXMYFAQFsbSr0fZ2RzpJyJyF0IIqBkXZccgJ1B8faEYjbJj6A5LvxOEhbD061G+xQZV1WTHICKif3CNfn0yBAXJjqBLLP1OEMQ5/bplsXBXXiIid8HSr0+GoEDZEXSJpd8JggJZ+vUqOcUiOwIREf2Dc/r1iSP9zsHS7wQmkwF+fpyLpkdnz+VCCCE7BhGR11MUBbbkFNkxyAk40u8cLP1OEswpPrqUmMTl4YiI3AVLvz4ZgoI4wOYELP1OEsJlO3UpKZmln4jIXdiSkmVHICcwBAUBGhfOqGws/U4SEsKRfj1KSsqHoiiyYxAReT01Lx+ioEB2DHICQ1AgwJH+SsfS7yTBQRzp1yOO9BMRuQdbUpLsCOQkPJDXOVj6nUDTBEf6dSo9o4Br9RMRSSaEgO30WdkxyEkMQYEAN+eqdCz9TqBpgnP6dUrTgLR0fp1MRCSbLZnz+fXKEBrCqbROwNLvBEJw9R49O3OGy3YSEcmkKAqsiSz9emWMjJAdQZdY+p1AUbh6j56d57KdRETScaRfv4xhYbIj6BJLvxMYjQrn9OtYYmKe7AhERF5NCME1+nVKCQyEYmaHcgaWfidQFAXhYb6yY5CTHDuezbmGREQSadk5gNUqOwY5gYlTe5yGpd9JoqL8ZUcgJzl+Ilt2BCIiryWEgPXcedkxyEmMEeGyI+gWS7+ThIf5wGzmj1eP8i02pKVZZMcgIvJaNpZ+3TJGRnCxDCdhK3USRVEQG8PRfr3adyCDL0pERJIUnTwlOwI5iTEiAlBV2TF0iaXfiapWDZQdgZwk4Rin+BARyaAoCqzHjsuOQU5ijOCcfmdh6XcSTROoGhsgOwY5ybHjWTyYl4hIAmG1ck6/jhmrRHI3Xidh6XcSVRWoGsvpPXqVcDxLdgQiIq8jhCie2qNpsqOQkxirRHJQzUlY+p3EaFRQjdN7dCs314aMjALZMYiIvE7RkQTZEciJOL3HeVj6ncRgUFCzOku/nh04dJEH8xIRuZCiKCjifH7dMgQFweDvJzuGbrH0O1F0NKf36NnRBE7xISJyNZZ+/TJVjZUdQddY+p3Ix8eIiHDuzKtXh49kct4hEZELqdnZUFPTZMcgJ2Hpdy6WfifjCj76dfDwRdhsPJiMiMgVhBAo3HdAdgxyIlPVWAibTXYM3WLpd7KqVVn69aqoSMNBzusnInKZwkNHZEcgJzJVjQX4DbrTsPQ7kc2mcaRf57bvTK302ywqKsJ7772H+Ph4REREwM/PDzVq1ED//v3x/fffOy6naRo2btyIGTNmID4+HpGRkTCbzahSpQr69OmD+fPnX/YDSV5eHiZNmoSYmBj4+fmhU6dOWLly5WUz/f7771AUBa+99lqlP14iovJQFAVFh1n69cxUozoUrtHvNIrgMKXT2GwaVq1JxOy5e2RHISdp3CgM78/tWmm3d+7cOfTr1w8HDx5ElSpV0LlzZwQGBuLs2bPYvXs3+vfvjx9//BEAcOzYMTRo0AAAEBERgfbt2yM8PBwnTpzAtm3bAAC33norFi1aBB8fnxL3M2TIECxatAjNmzdH3bp1sXz5clitVqxbtw6dO3cucdnc3Fw0bdoUYWFh2LFjB8xmc6U9XiKi8tIKCpE4+n6AtUW3qn3zOQz/er+iymOSHUDPTCYDatbgsp16djQhC4WFKnx9r39kwmKxoE+fPjh8+DBeeOEFTJ8+vUTBzs/Px9GjRx3/VhQFPXv2xNSpU9GnTx8YLxkdWbNmDW655Rb8/vvveOONNzBjxgzHebt378aiRYvQq1cvLFu2DEajEStWrEDv3r3xyiuv4Pfffy+R69lnn8X58+excOFCFn4ikkIIgcJDh1n4dcwQFsbC72Sc3uNkVWNZ+vVM0wR2702rlHn9r7/+Og4fPozx48dj5syZpQp2QEAAWrdu7fh3vXr1sGLFCtx8880lCj8A9OjRA8888wwA4Ouvvy5x3o4dOwAA9913n+N6vXr1Qv369bFx48YSl922bRs++OADPPTQQ6W+ASAicqXC3fzWXM9MVWNkR9A9ln4nCw31gV8ljAKT+9qx8/qXj7Narfj4448BAFOnTr3u2wOANm3aAADOnj1b4vT09HQAxVOCLhUZGYnc3FzHv202G8aPH49q1apxLj8RSaUoCizbd8mOQU5kqlqVC2M4Gaf3uEBsbABOnc6RHYOcZNeetOter3/nzp1IS0tDtWrVUL9+fezbtw8//fQTEhMTER4ejm7duqF///4wGMr/OT0hoXir+qpVq5Y4PS4uDgBw6NAh3HzzzQCKP3QcO3YM1atXd1xu7ty52L17N3799VcEBwdf1+MjIroe1uQUqKmVv3ACuQ9z1VhAVQETq6mz8CfrAnXrBLP069ip07nIzbUiKKji89337t0LAKhRowaeeeYZzJ49u8SIx6xZs9CmTRv88ssvqFWr1lVvLz8/H++99x4A4M477yxxXs+ePREcHIw5c+YgPj4eDRo0wMsvv4z09HTcddddAICTJ0/ixRdfxJAhQzBo0KAKPy4iouslhIBl42bZMcjJzLVrAdcwsEXXjj9dJ7PZNDSoFyo7BjnZ1u0XrutrSfuUm127dmHWrFl46KGHcOTIEWRlZWH58uVo2LAhdu3ahVtuuQVWq/Wqt/fQQw/h5MmTqFatGqZPn17ivCpVqmDWrFk4f/48OnbsiPDwcMydOxdNmjTBCy+8AACYOHEizGaz44ODXX5+foUfIxFRRSiKgoLtO2XHICcz142DwtLvVPzpOpnRqKBRozDZMcjJNm5Oua4pPvYPDFarFSNHjsQHH3yAhg0bIiQkBL1798by5cvh5+eH/fv3Y8GCBVe8rZdffhlfffUV/Pz8sHDhQkRGRpa6zMSJE7Fp0yZMmTIF48ePx4cffojt27cjPDwc8+fPx7JlyzB79mxUrVoV+fn5mDRpEkJDQxEYGIioqCjMmDEDqqpW+PESEZWXmp+PouMnZMcgJzKEhMAYEiI7hu5xeo+TKYqC+nVDoChcaUzPtm6/AJtNg8lUsc/Rl86ZnzBhQqnza9WqhVtuuQWLFi3C33//jXvuuafM25k7dy5mzJgBX19f/Pzzz+ja9fJ7CHTu3LnUijwZGRl44okn0K1bNzzwwAMAgLvvvhu//vorpkyZgvj4ePzyyy94+eWXkZeXhzlz5lTk4RIRlYsQAgWbtvINVOfMta8+bZWuH0f6XcDf38SdeXXOYlGxbUfFp/jUrVu3zP9f1mWSkpLKPP/999/H5MmT4ePjg0WLFjkO0r0WkydPRnZ2Nj777DMoioIjR47g559/xogRIzBr1iwMHDgQ//d//4dOnTrhgw8+KLHaDxFRZStetYdTe/TOHFcLQtNkx9A9ln4XaVCf8/r1bs265ApP8Wnbtq3jumlpZS8Baj89KCio1HkffvghHn30UUfhv+WWW645w6pVq/Dll19i2rRpaNy4MYDijbwAoEuXLiUuGx8fj6KiIhw8ePCa74eIqLyEzYbCfftlxyAnM9euxW9zXICl3wVsNo2l3wts2pICtYIjFbGxsYiPjwcA/P3336XOt1qtWLNmDQCgY8eOJc775JNP8PDDDzsK/6233nrN919YWIgHH3wQTZo0wbRp0xyn2z+I5OXllbi8/d/Xu1QpEdHlCCFQsO8ARFGR7CjkZD5160Axck8jZ2PpdwGjUUHDBiz9epefb8PuPekVnuIzc+ZMAMU7827e/L/l6Ww2GyZPnowTJ04gODgYY8eOdZw3b948PPTQQ9dV+IHig38TEhIwb948+FyyDXrbtm0BAPPnz4fFYgFQ/I3DTz/9BD8/PzRt2rRC90dEVB4FW7fJjkDOZjLBVDVWdgqvwAN5XUBRWPq9xarViWjbukqFrturVy+8/PLLeP7559GtWzd07NgRsbGx2LlzJ06dOgV/f3989913iIkp3qp89+7dmDBhAoQQqFu3Ln788Uf8+OOPZd72l19+edn7PXDgAGbPno0JEyaUOvC3fv36GDlyJL777js0a9YMbdu2xYYNG3DhwgVMmzYNgYGBFXqsRERXoygKLDt2y45BTmauUZ2j/C6iCO557DJ3j12JlAsW2THIiYKDzPjh294wGiv+Jdpff/2Fd955B1u2bEFOTg5iY2PRq1cvPP3004659gCwevVq3HTTTeW6zcv9mQshEB8fj1OnTuHgwYMIDS394bSoqAgzZszA/PnzkZKSgpo1a+LBBx/ElClTOL2HiJxCCAHrqdO48PRzsqOQkwX06IaISaVXraPKx9LvQi++ugPrNybLjkFONvu1TmjdMpKFmIiogoQQyPz8a+QtWy47CjlZ6Oi7EdSvNxQTJ584G+f0uwgP5vUeq9cmyo5AROTZNA356zfITkEu4NOoAcDpPS7B0u8iBoOChiz9XmH9xmRoGr9AIyKqCCEELJu3QuTly45CzmY0wieuNr8ZdxGWfhcxGBQ0asjS7w2ys63YuDmlwqv4EBF5M0VRkLditewY5ALmuNqc1uNCLP0uFBzsgyqRfrJjkAssWXaWIxdERBVgy8xE4QFu/OcNfOrX4068LsTS72IN6ofIjkAusGNXKrKyuKEMEdG1EEIgb+ly7s7qJXwa1udz7UIs/S5ks2moX49TfLyBpgG/LznNKT5ERNdCCOSvXic7BbmIb5NGXKPfhVj6XchgUNCkUZjsGOQiS5dzig8RUXkJIVBw4CDUjAzZUcgFDKGhMFWp2GaWVDEs/S5kMCho0SISRiOLoDdITrZg5+5UjvYTEZWDoijIW/a37BjkIr6NG8qO4HVY+l3Mz9eIhg04xcdb/Lr4NEf7iYjKQcvPR8GOXbJjkIv4NGoIYbPJjuFVWPpdTFU1tGnFr7O8xeatF5CZVSg7BhGRWxNCIG/FKkBVZUchF/Ft2pibcrkYS7+LKYqCdm1Z+r2Fpgn8upgH9BIRXYmiKMhbuUZ2DHIRxccH5tq1+E24i7H0u5jBoKBp43D4+PBH7y2WLDvDHXqJiC5DCIHCw0dhO58oOwq5iA9X7ZGCzVMCk8mAZk3DZccgF0nPKMRff5/jaD8RURkURUH2jz/JjkEu5Ne8GefzS8DSL4HNxnn93mbhohOyIxARuR0hBIrOnkPh3v2yo5AL+bZuyfn8ErD0S2A0KmjfNkp2DHKhc+fzsHFzCkf7iYguoSgKchYukh2DXMgQFARzrZqczy8BS78EiqKgXt0QBAaaZEchF/pu4TG+yBER/UMIAduFVFi2bpcdhVzIt1kTvhdKwtIvicGgoGWLSNkxyIWOHM3Cvv0ZHO0nIsI/c/l/+Anga6JX8W3ZnPP5JWHpl6R4Xj9Lv7f55rsEjnAQEQFQs7KQv36j7BjkYn6tWkIxcaaDDCz9kphMBs7r90I7d6fh5KkcjvYTkVcTQiD7x1+4GZeXMVaJhCma3UcWln6JatYIQni4r+wY5GLffHeUo/1E5NU0iwV5K1fLjkEu5tuiGQe9JGLpl6x1S07x8TbrNybjwgULX/iIyCsJIZD7y2LAapUdhVzMr0VzQNNkx/BaLP0S2Wwa2rTmev3eRtOAr+dztJ+IvJMoKkLusr9lxyBXUxT4tmrJnXglYumXqHheP0u/N1q+8hzOnc/jaD8ReRUhBHL/WAphsciOQi7mU78ujMFBsmN4NZZ+yaKq+CM21l92DHIxTQM+nneQo/1E5FVEkRW5fyyVHYMk8GvfDoIHbkvF0i+Zpgl06hAjOwZJsHXbBew/mM7RfiLyCkIIZC9cBC0nR3YUksC/Y3vAwNopE3/6buDGblVlRyBJPv7sEEf7iUj3hBBQMy4id8ky2VFIAmN0FMzVq/H9TjKWfskMBgXNmoZz6U4vdTQhC2vXJ3K0n4h0TVEUZH7+NcCdWL2Sf7u2EFy1RzqWfjcgBND1Bk7x8Vb/+eIISz8R6ZYQAoVHElCwbbvsKCSJX8f2siMQWPrdRg9O8fFaScn5+PX30yz+RKRbmf/5QnYEkkQJCIBv44ZQOJ9fOj4DbsBgUNCyeSRCQ3xkRyFJ5n93DEVFXNWAiPRFCIG8VWtgPX1GdhSSxK811+Z3Fyz9bkJRgC6dOcXHW2VlF+Hb749ztJ+IdEVYbcj+bqHsGCSRf/u2XKrTTbD0uwlNE5zi4+V+/PkEUlMtLP5EpAtCCOT8sAhaVrbsKCSL0Qi/dm050u8mWPrdhNFoQOtWkQgKMsmOQpIUFWl46529XNKMiDyeEALqxUzk/MklOr2Zb/OmMPj7yY5B/2DpdyMGg4IbOnGKjzfbtScdf688x9F+IvJoiqIg64uvAatVdhSSKKBrF07tcSMs/W5E0wR6xFeTHYMk+2TeIVgK+CJJRJ5JCIHCw0dh2bJNdhSSyWyGf+cOnNrjRlj63YjRaEC7tlUQ4M8pPt4sK7sIH31yQHYMIqKK0TRc/PQ/slOQZH6tW8Lgx6k97oSl382YTAZ06hgtOwZJtuzvczh46CKn+RCRRxFCIGv+AtjOJ8qOQpIFdL2BU3vcDEu/m7GpGlfxIQDAm2/vgaax9BORZxBCoOjESeT+sVR2FJJM8fWFf4d2nNrjZlj63YzJaEDH9tHw8+Mfirc7dz4P33x3jKP9ROQZVBUX3/0Q4GuW1/Nr3xaK2Sw7Bv0LS78bMpsN6Ng+SnYMcgPf/3Acycn5LP5E5Pay/vstbMkpsmOQG+DUHvfE0u+GbKqG7vGc4kOA1abhjTl7OHBGRG5LCIHCownIXbpcdhRyA0pgAPzatOLUHjfE0u+GTEYDbugUA39//sEQcPDQRXzzXQJH+4nIPdlsyHjvI07rIQCAf8f2gIH10h3xWXFTZrMBPbpxzX4qNn/BMSQkZLH4E5HbyfzyG6gXUmXHIDcR0C0e0DTZMagMLP1uSghgQL+asmOQm9A0gZff2ImiIo3Fn4jcghACBQcPI+/vlbKjkJswRlWBX/OmnNrjplj63ZTBoKBJ43DUqhkkOwq5ieQUC+a+txeKosiOQkReTggBUWTFxQ8+5rQecgi8sTsP4HVjLP1uTFU19O1dQ3YMciMrVydi5erzHO0nIqkURUHmF19DTUuXHYXchaIgsPdNHOV3Yyz9bsxoNODmvjVhNHJkl/7n3Q/3IyOjkMWfiKQQQiB/81bkr1wtOwq5Ed+WLWAMD5cdg66Apd/NhYb4cM1+KiE/34aXXt/Jb9SJyOWEELClXMDFDz+VHYXcTGCvGzm1x82x9Ls5VdXQv18t2THIzRw8dBH//ZbLeBKR6wghIKxWpL/+JkRhoew45EYMwcHw79COU3vcHEu/mzMaDejUIRoR4b6yo5Cb+fb7Y9i9N53Fn4hcQlEUZLz9AWxJybKjkJsJ6N4V4CITbo+l30P068MDeqkkTRN4+bWdSEsvYPEnIqcSQiB70S8o2LFTdhRyQ4G9e8qOQOXA0u8BFAUYdGscN7ijUnJyrZj2/FZYrVy/n4icQwiBwgMHkb1wkewo5IZ86teDuXo1KCwpbo/PkAdQFAVVIv3Qvl207Cjkhk6fycWrs3Zx/X4iqnRCCKiZWUif8x7X46cyBfS6EcLGA3g9AUu/h1BVDYNuqS07BrmpjZtT8NU3R2XHICIdEUIAqor012ZD5OXJjkNuSAkMQGC3rlBMPIDXE7D0ewij0YCO7aMQHeUvOwq5qfkLErBxczKn+RBRpVAUBRc/ngfr6TOyo5CbCux5I2AyyY5B5aT70i+EQO/evdGvX79S53300UcICwvDuXPnJCS7dpomMODmmrJjkJsSAnjjrd04n5jP4k9E10UIgdylfyF/3QbZUchdKQqCBtzMVXs8iO5Lv6Io+OKLL7BlyxZ8+un/NhM5efIknnrqKbz//vuoUcMzVsYxGg24tX9tmEz8A6OyWSwqps/YioICG4s/EVWIEAJFRxOQ+dV82VHIjfm1bwtTZASPJ/Mgui/9AFCzZk28++67mDJlCk6ePAkhBMaNG4e+ffuiTZs26N+/P4KCghATE4N77rkHaWlpjuv++OOPaNGiBfz9/REZGYnevXsjT+LcxtBQH3TpHCPt/sn9JSXnY+bLO6BpgsWfiK6JEAK2xCSkvf4WwN1V6QqCb+nPHXg9jFeUfgAYPXo0evXqhfvuuw8ffPAB9u/fj08//RQ9e/ZEmzZtsH37dixduhQpKSkYNmwYACApKQkjR47Efffdh0OHDmH16tUYPHiw1CKlqhruvKOutPsnz7BrTzreeGu37BhE5EGEEFAzLiL1xdcg8vNlxyE3Zq5VE75NG3MHXg+jCC8aCrxw4QKaNWuGjIwMLFq0CPv378e6deuwbNkyx2XOnTuHmjVr4siRI8jNzUW7du1w6tQp1K7tXivnPD51Iw4cvCg7Brm5226tjYcnNpcdg4jcnBACWl4+Lkx7HmrKBdlxyM2FTRiHwBu7s/R7GK8Z6QeA6OhoTJgwAU2aNMHtt9+OPXv2YNWqVQgKCnL817hxYwDA8ePH0apVK/Tq1QstWrTA0KFDMW/ePFy8KL9o22waRg6rLzsGeYBffz+N/37LpTyJ6PKEEBBWK9JefJWFn67KEBSEwO7xLPweyKtKPwCYTCaY/lleKjc3FwMHDsTu3btL/JeQkIDu3bvDaDRi+fLlWLJkCZo2bYr3338fjRo1wsmTJyU/BgM6dYhGXO1gqTnIM3w9PwGL/zjN+f1EVIoQAtA0pL06i0tzUrkE9roJYOH3SF5X+i/Vtm1bHDhwAHFxcahfv36J/wIDAwEUr/7TtWtXvPjii9i1axd8fHzw888/S05ePNo/fCjn9lP5fPDJfqzbkMTiT0QlCYH0Oe+i6NAR2UnIExgMCBrQj8t0eiivLv2TJk1CRkYGRo4ciW3btuH48eNYtmwZxo4dC1VVsWXLFrz22mvYvn07zpw5g59++gmpqalo0qSJ7OgwmQzo2aM6YqK5WRddnaYBb7y5B3v3Z7D4E5HDxU/+g4LtO2XHIA/hf0MnGMPDuEynh/Lq0l+tWjVs2LABqqqib9++aNGiBR5//HGEhYXBYDAgJCQEa9euxYABA9CwYUM899xzmDNnDvr37y87OoDir2WHDOZoP5WP1abh+Re34cTJHBZ/IkLmf79D/uq1smOQp1AUhNx5O4SmyU5CFeRVq/fokdWqYdToFcjMKpIdhTxESIgZ783pgmpVAzlaQ+Slcn77A1nffCc7BnkQv3ZtUeXpJ2XHoOvg1SP9emAwKLhtYJzsGORBsrOteGzyJpw5m8sRfyIvI4RA7opVLPx0zUKG3M7NuDwcS7+HMxoV3DEoDv7+PJKeyi8ruwiPT92Ik5zqQ+RV8pb9jczPPpcdgzyMb7Mm8KlXl8t0ejiWfh3w9zdhQL9asmOQh8nNteGJpzbhaEIWiz+RF8j57Q9kfv4VwL93ukbBd3KUXw9Y+nVAUYDhQ+rBbOLTSdcm32LDlGmbceDQRRZ/Ih3L/uEnTumhCjHXqwu/5s04yq8DbIk6oCgKwsN90fOmarKjkAcqKFDx9LNbsGtPOos/kQ7lLl+B7B9+kh2DPFTI4Ns4yq8TLP06oWkCo4bVh4HPKFVAUZGG52Zuw5atF1j8iXRACAEhBAotVgTEd4G5NqeA0rUzVa8G/w7tOMqvE6yIOmEwKKhWLRBdOsfKjkIeymrT8MKrO7BmHXfuJfJkQghoGvD6m7sxZsJaFMGIKjOmwVSV7w90bYLvGMRRfh1h6dcRVdUwanh92THIg6mqwOtv7sLvS87IjkJEFSCEgM2m4bkXt2HVmkSkpRfgwcc2QPPxQ9TMZ2GMjJQdkTyEqWpVBMR34Si/jrD064jRaECD+qFo27qK7CjkwTQNeO/D/Zj3+SEA4Kg/kYcQQsBisWHyM5uxfUeq4/Tz5/Px6FObgaBgRM2cDkNoiMSU5ClCht/JlZ50hqVfZ1RVwwP3NQY3WqXrtXDRCbzyxk5ommDxJ3JzQggkp1jw0GPrcehwZqnzjx3LxtMztsFQJRJRz0+DEhDg+pDkMcy1ayGgS2eO8usMS7/OGI0G1K8Xih7dqsqOQjqwZl0SpkzbDEuBjcWfyE0JIbBnbzomProO5xPzL3u5Pfsy8OLru2CsVg1Vnn0Kiq+vC1OSJwkZOYxz+XWIpV+HNE3ggbFNuG4/VYr9By5i4iPrkZxiYfEnckO//HYKTz+3FXl5tqteduPmC3jrvX3wqVsHkU89AZhMLkhInsSnQX34t23NUX4dYivUIYNBQVSUH24dwCXaqHIkJuVj4qPrsHsv1/IncgfFK/QIvP3+Xnz02UFoWvn/LpevOI+P/+8wfJs1RcRjk8C1nulSoXeN4Ci/TvEvXcfuvashAgI4ikOVIy/Phmee24rf/jgNgAf4EsliP2B36rTN+HPp2Qrdxs+/nsI3C47Bv0M7hD/4AHggGAGAb4tm8G3amKP8OsXSr1OKoiAgwIQRQ+rJjkI6omkCH3x8AG+/vxeqygN8iVxNCIHEpHxMeGQd9u7PuK7b+np+An794wwCesQjdMw9lZSQPBlH+fWNpV/HDAYFQwbXRZVIP9lRSGf+XHoWDz+xAWlpBSz+RC4ihMD2nal46NH1SE62VMptfvjJAaxcnYjg/n0RMnRwpdwmeSa/9m3hU7cOR/l1jKVf5wwGYPTdDWXHIB06fiIb909ci/Ubk2VHIdI1IQRUVeA/XxzGszO3Id9y9QN2r8Ubb+3G1m0XEDJ0MIJu6V+pt00eQlEQOmo4hKbJTkJOxNKvc0ajAX1710Bc7SDZUUiH8i02vPTaTrz/0X6oqsZRf6JKJoRARkYhHp+6EQsXnXDaXknPvrANBw5mIGz0XQjoeaNz7oTcVsBNPWCuUR0KD+rWNT67XkCI4iU8iZzltz9O45EnNyA9vZDFn6gSrduQhHET1+DwkUyn39fjUzfh5MlshE8YB/8bOjn9/sg9KP7+CL1rOF+7vQBLvxcwGg3o2CEaLVtEyI5COpZwLBv3P7QGGzenyI5C5NGEELBaVcx9dy9efn1XudbfrywPProOycn5iHj0Ifi1aeWy+yV5gu8YBENgIBSu4KR7iuBHO6+gqhpOnMzBQ4+tlx2FvMDtA+Pw4ANNYDAofCMhugbFq/PkYcZLO3DmbK6UDGazAd/8340IDzEh9ZU3UHToiJQc5HzG6CjEvvMmFG7S5hU40u8ljEYDGtQPRff4qrKjkBf4ZfEpPPjIepw8lcOvjInKwf538vuSMxg/aZ20wg8AVquGsQ+uRa5FQ5VpT8FcJ05aFnKu0HtGcY8GL8KRfi+iaQKpaQUY88Aq2Gx82sn5DAYFw+6sizH3NOSoP9FlCCGQk1OEOe/uc6vpceFhPvjqsx7whRUXnnsRtvOJsiNRJfJp0hjRLz4nOwa5EEf6vYjBoCA6yg+39K8tOwp5CU0TWPDDcYyftA7HT2Rz1J/oEva/hyXLzuLe+1e7VeEHgIuZRZjwyHrYjD6ImvksjFFRsiNRZVEUhN13Lzfi8jIc6fcyQgjk5tkwetwq5ORaZcchL2IwKBhyRx2MvbcRjEaO+pN3E0IgLa0As+buwZ696bLjXFFcXDA+frsLkJWJC8++AC0zU3Ykuk4BN3VHxMTxsmOQi3Gk38soioIAfxPuH9tYdhTyMpomsHDRCYyftBYJx7I46k9eSQgBTRP4/sfjGDN+tdsXfgA4dSoHk6dvhRIWhqiZ02EI4r4vnkzx80PoXSP5GuyFONLvxR6fuhEHDl6UHYO8kMEA3DGoDu4f25ij/uQ1hBA4fSYXb7y1G8dPZMuOc806tIvCKzPawXb6DFJffBWioEB2JKqA0LuGI2jgLdyIywux9HspVdWQmJSP8ZPW8qBekiY6yh/jxzVGj27VIIRg+SddEkJAVQU+/+oIFv1yEprmua+5N/WoimmTW6HoSAJSX50FWDlN1JOYatZAzOxXoRiNsqOQBCz9XkzTBL765ii+/f6Y7Cjk5Vo0i8BjDzdH7VrBLP+kG/bf5fUbk/DZ/x1GUnK+7EiV4tYBtfDoxGYo2LUH6W+9A/BgUM+gKIh6ZSZ86tZh6fdSLP1ezmrV8MBDa3A+UR9vRuS5DAagX5+amDCuKQICjCz+5LHsZf/EiWy8/8l+7D+gv2mUo4bVw5h7GsKyaQsy3vsIYJVwe4G9bkT4hPtlxyCJWPq9nM2mYf+BDEydvkV2FCIAQGCgCXePaIDBt9eBooDlnzyKEAJZWUX4eN5BrFqTqOsuPH5cYwy5oy7ylq9E5n++kB2HrsAQEoLY996C4u/P11QvxtJPAIBZb+3G36vOy45B5FCjeiAmTWiK9u2iOeWH3J4QAlarhv9+m4Cffj2JoiJNdiSXmPxYS9zctyayf1mM7G+/lx2HLiP84QcR0PUGTuvxciz9BE0TyMuzYfQDq5CTw4OyyL10aBeFcWMboV6dUJZ/cjtCCAgB/LnsDL765igyM4tkR3K5mc+2RXyXqsia/z1yfl0sOw79i2+zpoiaOV12DHIDLP0EoHg1n+UrzmPOu3tlRyEqU6cO0Rg3phHqxIWw/JN09t/BTVtS8PlXh3Hq9P+3d+fxUdT3H8ffM7NH7mtzACHhTLghoFxVQaTaWjyqVlvvn1KPilUpIIoKUlQ8W1tb21r7q/Ws/anV2sP7AFsrCoiAnMoZQEISQu5kd+b3RwhFhQrkmN3Z1/PxyEMNEj7AZvc1s9/5To3bI7nqnvljVDI0pMqHH1HtK6+5PQ5a+Xzq8pO7ZOVkc5YfRD8+b9rMd/XRigq3xwAOavTIXH3/kn7q2YP4R+drfclc+M52Pf7H9dqwsdrliaLHgz87VkV901XxwK9Ut/Cfbo8DSanfOUNp3zmDPfkhiejHfiIRWzs+q9dlP1ig5nB8rEdF7Bo9MleXXtxPvXsR/+h4rct4Xn9zq558+hNtLa11e6SoY5rS7349Xvldk1R+z/1qWLzE7ZHimq9rF+Xdd6cMn8/tURAliH58jm07euzJdXr8qXVujwIcktEjc3XJRcXq05s1/2h/juPIth397aUt+tMzn+iznfVujxTVfD5Djz18vEKZAe264x41rljp9kjxyTCUM2+2An16s6wH+xD9+JJw2NZlVy3gTBZiysijcvSdM3tpREkO8Y82aX1ZDIdtvfDXTXrmuU9VXtHo8lSxIzHBp8d+N16piaZ2zb1dTes/cXukuJNyyslKv/A8ngfxOUQ/viQcsfXxqkpNm/lvt0cBDlv3/GSddkoPTfpmofz+lnWsvPDhULQeLFZWNur5Fzfqb//YrKo98bcbT3tIS/Pr0d8er0QzrLLZ89S8eYvbI8UNX7euyrt3Pst68CVEPw7q3p8u08uvbXV7DOCIJCZaOmlid519Zm/l5SVx9h8H1foyuGRpmZ5/cZMWfbBTNpc1tVl2doIe+fVx8jU3audNtyry2Wduj+R9pqnc2+fK37OQZT34EqIfB2TbjhobI7p8ygLt+Iw1rIhdhiGNKMnWmd/upZFH5ez9HPEf71oPAmtrw3rx7xv1t5c2a8cOnuvaW2FBsn79s2Nk1FSr7KZbFalgd7iOlHr6qUo77xye43BARD8OKhyxtW5dla6b8a5sm4cJYl/XLkmadHKhvnlSgdLTApz9j0Otf+er1+7Wc89v0Dv/3MFuZR2suChdP79njOxdu1R281zZ1Wxz2hF8Bd2Vd9dtLOvBQRH9+K8cx9GjT7CbD7zFNKWhg0OaeEK+JozrpmDQ4gDAw1r/bndXNeq1N0r18qtbtXET4dmZRpSENH/u0QqXblPZ7Hly6nlXpV1ZlnLn/1j+gu4s68FBEf34SrbtaOr17+rjVZVujwK0O7/f1JhRuTpxYr5GHZ0r02wJfw4AYltr6NfUNOu1N0v11oJt+nhVpXjFc88xX+uiOTeUqGn9J9o17045TVwk3V5Sz/q20s45i+ct/FdEP75SJGKroqJR379qgerqwm6PA3SYlBSfjjumq75xYoEGDcjcd4EnL6SxoTX06+vDev3NUr21cLuWryjnotwocvJJ3TX16sFqXPGxdt15rxSJuD1SzPP3KFTunfM4w4+vRPTjkEQitt5asF133vuh26MAnSInO0GjR+Vq7Kg8DS8Jye9nCVC02f+grL4+rLcXbtfbC7dp6bJyRSK8tEWrs8/spcsu6a/6RR+o4qcPiLdfjpwRCCj37tvly8sl+vGViH4clvn3LNUbb21zewygU/n9poYNydKoo3M17tguCoUSeRfAJfv/uW/aXKN/vbtDixbv1KrVuwn9GHLJRf107jl9VPfmAlX+5mHC/whlXHaJkidOkGGabo+CGED045DZtqOmpoguu4ptPBHf8rsladTRuRo7OldDh4RkWSYHAR1k/z/X6ppmvf/BTi1ZukvvLy5TRSV3yY1l11w1WKdO6qHqv/1DVX94wu1xYk7i6JEKTbvW7TEQQ4h+HJZwxNb69VW6djrbeAKSlBC0NHRIlgYPzNKwoVkqLsqQz8dBwJHafwlVfX1Yy5aXa8nSXVq6bJc2bqpxeTq0t1nXD9eE8d1U9adnVf3Mn90eJ2ZY2SHl3XenjGCQs/w4ZEQ/DpttO3rsSbbxBA7E7zNVVJSuwQMzNaB/poYMylJ6ekCSOBD4gv3/PMJhWxs2Vmv5igqtWbdba9buVum2OpcnRGe448cjNfKoXO1+5DHV/P1lt8eJfqapnB/fokCf3qzjx2Eh+nFE2MYTOHShUFD9ijLUv1+Givqkq0+fNGVmBPf9uNcPBr74+7NtR9u21Wr5ygqtXrtba9ZWaeOmatbkx7Gf/+RrGtAvUxUPPqS6txa4PU5USzvnLKWe9W3PPl+g4xD9OCJs4wm0TULQUvfuySosSFFhQYp6FKaob5905WQnyLJa3q7f/+k52l/gD3TgUlcX1rbttfp0Q7VKt9WqdFuttpbWqrS0Vg2NbNWIz3v4V+NU2D1ZFT99QPXvve/2OFEpOHCAsufMivrnA0Qnoh9HjG08gfZnWYbyu7UcDOTkJCiUlaCszKBycxKUl5uo9PSgEhN9n/s5juPIcaQDdcDhxsHBXhK++HVs21F9fVh79jRp564GlZc37A37upZ/ltaquqb5sH5txDefT3rkoQnKzQ5q1533qnHZcrdHiipmaory7rtLZmoKy3pwRIh+tBnbeAKdy+83lZUZVFZWUKGsBGVmBpWUaCkYtBQIWEoIWgoELSUETSUm+pSU5FPi3s8FA5YitqNws63miK3m5r0fTRE1NNpqarIVDrd8NDfbqtrTpN27m7S7qlG7q/7z7zU1zdz0Cu0uEDD1+P9OUEaKpbIfz1fTmrVujxQ1QjOnKaFkKMGPI0b0o01at/G88ocLuegOANBmKSk+Pfbw8UryOyqbc5uaN2x0eyTXpZw6SRkXnuv2GIhx7POENjFNQz6fqdtuHamkLyw5AADgcNXUhDX5BwvUZJvKueUG+bp1dXskVwUHD1L6+d91ewx4ANGPNvP5THXtkqQbZpQccE0xAACHo6KySZdf80+FfUHlzLlJVnbI7ZFcYYVCCv3oGrfHgEcQ/WgXlmVqzKhcXXhekdujAAA8YPv2Ol074105ySnKufUmmenpbo/Uufx+ha6fKiMxgRtwoV3wKEK7MQxDF55XrGO/1sXtUQAAHrD+02pdf/MimZlZypkzS0ZystsjdZrMyf8jf49CLtxFuyH60a5s29EN00vUs0eq26MAADxg+cpKzb5jqawuXZRz80wZweBX/6QYlzxxgpJPGM8ZfrQrHk1oVy0X9hq6/daRSk31uz0OAMAD3lu0U3ff/5H8PXsoNHOa5Pfu60ugbx9lTL74oPfMAI4U0Y92Z1mmQqGgZt84QqbJlb0AgLZ7/c1t+uVDqxQc2F+hqT+UPLjsxUxPU+j6qZJhcNddtDuiHx3CskwNGxrS5ZP7uz0KAMAjXvjrJj365DoljChR5lWXH/g21LHKshSaeo3M1FTW8aNDEP3oMIZh6Kxv99aJE/PdHgUA4BGPP7Vef/7LJiUd+zVlXHqx2+O0m4zv/48C/YsJfnQYoh8dynEcTf3hUPUrjrOt1gAAHeZXv/1Yr71eqpRvfF1p3zvb7XHaLOXUbyll4gQu3EWH4tGFDmUYhkxTmjd7pLIyvb/jAgCgc9z902V6970dSjvzdKWcNsntcY5YwsijlH7BuW6PgThA9KPDWZap1DS/5s4+Wn4fDzkAQPuY/ePFWr6iXBkXnKvkr5/g9jiHzd+rp0LXTpHYqQedgAJDp/BZpor7puuHUwa5PQoAwEN+NPPf+uTTKmVcdokSjxnr9jiHzMrKUvas6yXLYlkPOgWPMnQa0zR08kmFOuO0nm6PAgDwkKuufUfbt9cq6+orlTCixO1xvpIRDCp71nSZKclcuItOQ/Sj0/3g8oGaML6b22MAADzCtqXJP1ioisomhaZfp+DAAW6PdHCGoazrrpYvP5/gR6ci+tHpHEeaOa1ER4/IdnsUAIBHhMO2LrligaprwwrdOF3+Pr3dHumA0i88TwkjSgh+dDqiH53ONA0ZhjT3lqPVv1+G2+MAADyiviGsS69cqIawoZybb5CvoLvbI31OysknKfWUk7nbLlxB9MMVpmnIskzNnzdKhQUpbo8DAPCIqj1NumzKQjWbfuXMniUrL9ftkSRJicd+TRmXXOT2GIhjRD9cY1mGEhIs3X3HaOXkJLg9DgDAI3aWNWjKj/4lJyFROXNukpmZ6eo8CSVDlTXlCjlszQkXEf1wlc8ylZ4W0D13jFFamt/tcQAAHrFpc42m3viejPR05cyZJTPVnXeVA0V9FZoxVTIMlvXAVUQ/XOfzmeqSl6g7541WYiIXNgEA2sfqNVWaNXexrNxcZd9yo4zExE799X3d85V9E3vxIzrwCERUsCxTvXulau4t3LUXANB+lizdpTvuXSZ/QXdl3zhdRiDQKb+ulR1SzuwbZQSDBD+iAo9CRA3LMjVsSEgzpw8Tz48AgPby9sLtuv/BjxUoKlLW9OukDt4u00xNVc7sWTJTU9maE1GDtEJUMU1D447tqquvHOz2KAAAD/n7S5v1uz+sUcLQwcq65iqpg9bXGwkJyr55pqycbIIfUYXoR9QxDEOnTuqhC88rcnsUAICHPP3sp/rTs58qccwoZVx+abt/fSMQUPYN0+QvLCD4EXV8bg8AHMxF5xdrz54mvfDXTW6PAgDwiIcfWaOUVL8mfXOCnLo6VT32VPt8Yb9foRumKdCvnwyLc6qIPkQ/otqUKwdpT3Wz3nx7m9ujAAA84v4HVig1JaBxp06SXVun6udeaNsX9PuVPXOaggMHcNEuohaPTEQ1x5FumF6iCeO7uT0KAMBD5s1foiVLy5T+vbOV/M0Tj/wL+XwKzZiq4OCBBD+imuFwezhEudaH6L33f6RXXtvq8jQAAC/5xU+PUb/iDFX88jeqe3vh4f1kn0+hGdcpYdhQgh9Rj+hHTHAcR4Zh6Ge/WK6//mOz2+MAADzkd78ep4L8ZJX/5OdqWPTBof0ky1Jo+nVKGD6M4EdM4FGKmNB66/Jrrx6iM07v6e4wAABPueLqBSrb1aDQdVcrOOQQtoy2LIV+dA3Bj5jCIxUx56rLB+l7Z/dxewwAgEeEw9LkH7ytqpqwsmf+SIHi/7JltGUp67qrlXDUcIIfMYVHK2LS5P/pr4vOZx9/AED7aGiwNfnKt1XX6Cj7puvl71H45f9pb/AnjjyK4EfMYU0/YtrTz3yih3+/2u0xAAAeEcoK6pGHxskfblTZzXMV3r6j5Qf8foVmTFXC0MEEP2IS0Y+Y9/yLG/Xgb1aKRzIAoD3k5yfptw8cK7O2RjtvulV2ba2yb5yhQL8igh8xi+hHzHMcRy+9skX3/2K5bNvtaQAAXtC3b5p+cc9YOZUVsmtq5e9ZKMOy3B4LOGJEPzzBcRy9/tY23fOTZbJtHtIAgLYbd2wX3Xx9iSQR/Ih5vEcFTzAMQyeM76abZg6XZRlujwMAiHHduibpiskDFZFB8MMTONMPT7FtR++9v1Pz7lii5jBrfQAAh69P7zTdddtoJSf75PNxfhTeQPTDc2zb0YfLdunW2xervj7i9jgAgBgyZFCWbp87UoGAKcsi+OEdRD88KRKxtXlLjW68ZZHKKxrdHgcAEAPGjMrV7FlHyTQNlorCc4h+eFY4bKuqqkk33PKeNm6qcXscAEAUO3FivqZfN0ySZJoEP7yH6IenRSK2mppszZn3gZYuK3d7HABAFDr3nD669OL+chxHhkHww5uIfniebTtyHOm+ny3Tq6+Xuj0OACBKBAKmpl07VCccn+/2KECHI/oRF1rP3vzh8bV6/Kl1bo8DAHBZVmZQ8+Ycrb590lnOg7hA9CPuvPL6Ft3/8xVs6QkAcapvnzTdPneU0tL88rFDD+IE0Y+4Y9uOVq2u1Jx5i1W1p8ntcQAAnei4Y7rohuklsiyDLTkRV4h+xKVIxFZFRaNmzVnEzj4AECcuOLevLr6gn2zbYUkP4g7Rj7gVidhqbrY1b/4SLfqgzO1xAAAdJBg0NWPqMI0/rpvbowCuIfoR12y75eH/m9+t0nPPb3B5GgBAewuFgrptzkj16pnGDbcQ14h+YK+/v7RZD/xqhcJhviUAwAuKi9J1+60jlZLil8/H+n3EN6If2Kv1At9585eovKLR7XEAAG0wYXw3zZg6TKYpLtgFRPQDnxOJ2KqtDeu2u5Zo6YfcwRcAYo1hSBedX6wLzi3igl1gP0Q/8AW27cgwpMeeXKcn/rhONtv5A0BMSEiwNHNaiY4ZmyfDIPaB/RH9wEE4jqOPllfo9ruXqrKS5T4AEM369E7TLTeOUJe8JC7YBQ6A6Af+i0jEVnVNs267c6mWfcRyHwCIRqef0kNXXjZQhsH6feBgiH7gK0QitgzD0KNPrNWTT68X3zEAEB1SU/2acd0wjR2T5/YoQNQj+oFD5DiOli4r1/y7l2p3VZPb4wBAXBsyKEs33TBcGekBzu4Dh4DoBw5DJGJrz55mzZu/RMtXVrg9DgDEHdOUzvtukS48r0iOI9bvA4eI6AcOUyTSsrvP7x9do6ef+YTlPgDQSbJDCZo1c7gGD8xkdx7gMBH9QBu8v3in7rz3Q+3Z0+z2KADgaWNH5+r6aSVKSLDkYzkPcNiIfqANIhFbu6uaNO+OJVq5qtLtcQDAc/x+U5dd2l9nnNaLm20BbUD0A23Uutzn8afW6ak/rVc4zLcUALSH7vnJmj1rhHoUphL7QBsR/UA7sW1Hm7fU6K77PtT6T/a4PQ4AxLQTJ+br2ilDZFmGfD6W8wBtRfQD7ah1T/8nn16vJ/+4Xs1h2+2RACCmpKb4NeXKQZo4IV+O43DBLtBOiH6gA9i2o62ltbrr3g+1dn2V2+MAQEwYd2xXXTtlsJKTfey9D7Qzoh/oIK1n/Z9+5hM99uQ6NTdz1h8ADiQUCuraKUM0dnQeF+sCHYToBzqYbTvatr1Wd923TKvX7HZ7HACIGoYhTfpmoS6fPED+gMlWnEAHIvqBTtB61v//nvtUjz6xVk1NnPUHEN+65ydr+nVDNWhgFmv3gU5A9AOdyLYd7fisTnfdt0wfs68/gDhkWYbOOau3LjyvWIYpzu4DnYToBzpZJGLLNA09+/wGPfLYGjU2ctYfQHwoLkrXjKnDVFiYIpMz+0CnIvoBl9i2o50763XXTz7UipWc9QfgXQlBSxdfUKwzv91LjuOwMw/gAqIfcFHrWf9/vLxF//uHNara0+T2SADQroaXhDT92mHKzk5gVx7ARUQ/EAUiEVuNTbb+8Pha/eWvGxUO820JILalpvh15WUDdNLXCxSJ2JzdB1xG9ANRovVbcdv2Ov3y1yv1/uIylycCgMNnmtKJE7vr8ksHcJMtIIoQ/UCUaT0j9v4HO/XgQx9ra2mt2yMBwCEZXhLSVZcPUs8eqdxkC4gyRD8QpcLhlr39//zCBj3+x3WqrQ27PRIAHFBhQYqumDxAo0bmspQHiFJEPxDlIhFHtXXNevj3q/Xyq1tks8MngCiRnhbQRecX6ZRv9ZBtO/L5iH0gWhH9QAxofZt8w8Y9euDBlVq+ssLtkQDEMb/f1Bmn9dQF5xYpEDA5sw/EAKIfiCGtb5u//c42PfTwau0sq3d7JABx5vhxXXX55AEKZbEFJxBLiH4gBoUjthxb+uP/rdefnvlUDY0Rt0cC4HED+mdoyhWD1K84g4t0gRhE9AMxzLYd7a5q0mNPrNXLr25Vc5gF/wDaV5e8RH3/kv4af1w3LtIFYhjRD8Q423ZkGFJFRaMef2od8Q+gXSQn+3Ted/vqzNN7SYbkI/aBmEb0Ax5B/ANoD8nJPp1+Sk+dfVZvJSb4ZFks4wG8gOgHPIb4B3Ak0tL8OvP0Xjrz9F4KBi3W7AMeQ/QDHrUv/iv3xv8rxD+ALwtlBfWdM3rrtFN6yLJMzuwDHkX0Ax5H/AM4kLzcRJ3znT761jcKZBjiAl3A44h+IE4Q/wAkKb9bss77bh9NnNBdkkPsA3GC6AfiDPEPxKeePVJ1/vf6atyxXWU7DrvxAHGG6Afi1P7x/+yfN+ilV7aouqbZ7bEAtLPionRd8L0ijR2Tp3DEJvaBOEX0A3HOcRw5jhQO23r1jVI9/5eN2rip2u2xALTR4EGZuvDcIo0YnqNw2JbPR+wD8YzoB7BPaxgsX1mh557foH/9+zPZNk8RQKwIBk1NGJ+v00/tob690zmzD2Afoh/Al0QitizLVHl5g55/caP+/vJm7dnD0h8gWhUWpOiUkwv1jZMKlJhgyXHEPvsAPofoB3BQrUt/IhFHb7xVqudf3Kj1n+xxeywAknw+Q8eM7aLTTumhoYND+w7WAeBAiH4Ah6R16c+q1ZV69vkNeudfOxSJ8PQBdLbcnERNOrlQk04uVHpaQJGIww21AHwloh/AYWk9m1i5u1EvvLhRf3tps3bvbnJ7LMDTTFM6ekSOTjulp0YelSPHEaEP4LAQ/QCOWCTiyHEcLXhnu159fauWfFjOhb9AO8pID+gbJxXo9Ek9lJOTyIW5AI4Y0Q+gzVqX/lTtadIbb5bq9bdKtWZtldtjATFryKAsnTqph447totMw5BhSIbBmX0AR47oB9CuWg8Atu+o06uvb9Ubb5WqdFud22MBUa+4b7rGj+uqE47PV3Yogb31AbQroh9Ah3AcR7bdsu543foqvfLaVr21cBvr/4H99O6VquOP66YTju+mvLwklu8A6DBEP4AOt/86/6Uf7tJrb5Tqn//eofr6iItTAe4oLEjR+OO6auKEfOV3S2arTQCdgugH0KlaA6epKaJ/vrtDr7+5TR8sKWP7T3hat65JOn5cyxn9HoWpikRsmabBOn0AnYboB+Ca1jXLNTXNevPtbXr3vc+0bHm5mppst0cD2iwvN1Hjj2tZo9+nd5oiEUemyQW5ANxB9AOICq0HAM3NtpYu26X3Fu3UosU7tWNHvdujAYesS16ixo7J08Tj89WvOEO27bDzDoCoQPQDiDqRiC3DMGSahrZtr9W//v2ZFn2wUytWVKo5zLsAiB5+v6lhQ7I08qhcjRmdq25dk/ddw2KahD6A6EH0A4h6re8CNDZGtHhpmd57f6fe/6BMZbsa3B4NcahrlySNOjpHo0bmqmRoSIGAxfaaAKIe0Q8gpux/AeSmzdV6973PtOj9Mn28upKLgdEhUlP8GjY0pOEl2Rp1dI665CXJtlvuRs2uOwBiBdEPIGY5jqNIxJHPZ6quPqwPFpdpydJdWr6yQlu21ohnNxyJYNDU4IFZGl6SrZFH5ahXz1QZhsHZfAAxjegH4BnhiC1r77sAtbXNWrGyQstXVmjFykqtXVfF9QA4oNRUv/oVZ6h/cYZGDM/WgH4Z8vlMhcO2LIttNQF4A9EPwLNalmC03BW4udnW2vVV+mh5uVatrtTqtVWqrGx0e0R0soSgpb5909SvKEP9+2Vo4IBM5eYkSvr8BeQA4DVEP4C4sf9yIEkqL2/QylWVWrVmt9as2a1166vU0Mhdgr3Csgz17JGq/sUZ6lecroEDMlXQPUWmabAmH0DcIfoBxLXWi38tqyUEt2yt0cqPK7VhY7U2b6nR5i012lXOLkHRzjCkbl2T1a84Xf2LMzSgf4b69E6X329+6WAPAOIR0Q8AXxAOt+wQ1LrMo6EhrK2ltfp0Q7U2b205ENi8uUbbd9Tt25MdncPvM9W1a5K65ycrv1uy8vOTVdg9RX16pykpySdJrMUHgAMg+gHgEEUitiRDltUSk+Gwre076vTphj373hXYvKVGW0tr1dTERcNHyjQNdclL3Bf23fOT1b17igq6JysUSpC5N+YjkZYlOpzBB4CvRvQDQBtFIrYcR/vi03Ecle1q0MZN1dq5s17lFQ0qr2hURUWjKiobVFHRqMrdTXH9LkFSkk8Z6QHl5SX+J+zzU1RYkKLcnIR9a+1t21HEduTjzD0AtAnRDwAdpHUtuaQvLTexbUfV1c2qqGxQWVnDfw4MKhtVUdFyYLCrokGVlY0Kh6P/aTohaCk9PaCMjIAy0oPK2O/f09MDyswMKpQVVHp6UGmp/s+dnSfsAaDjEf0A4LL9Dw72v5agVU1tsxrqI2psjKihMaKGhrDqGyJqaGj5XFNTy+ebmmw1NLT8d2NjRI1Ndss/Wz+aIjJkyOc35PeZ8u398PtN+Xz7fc5vyu8zWn5s739/8cdTU/wKZQWVkdES8YGA9aXfVzjc8g6IaYpdcgDAZUQ/AMQox3H23YvAkSSnZReblo/D32/ecfZ+LefzX3MfQzLU8vVNk7PyABBLiH4AAADA43i/FQAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwOKIfAAAA8DiiHwAAAPA4oh8AAADwuP8H5yIjqy7Ap7wAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calculate the counts for 'Yes' and 'No' responses\n",
        "meter_box_counts = df_temp['4.1 Does this building have an electrical meter box?'].value_counts()\n",
        "\n",
        "# Calculate percentages\n",
        "meter_box_percentages = (meter_box_counts / meter_box_counts.sum()) * 100\n",
        "\n",
        "# Plot a hollow pie chart\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "ax.pie(\n",
        "    meter_box_percentages,\n",
        "    labels=meter_box_percentages.index,  # Use 'Yes' and 'No' as labels\n",
        "    autopct=lambda p: f'{round(p)}%',  # Round data labels to the nearest integer\n",
        "    startangle=90,\n",
        "    wedgeprops={'width': 0.4, 'edgecolor': 'w'},\n",
        "    colors=['#444ab4', '#e3555b']  # Professional color palette\n",
        ")\n",
        "\n",
        "# Add a circle in the center to make it a hollow pie\n",
        "center_circle = plt.Circle((0, 0), 0.6, color='white', linewidth=0)\n",
        "ax.add_artist(center_circle)\n",
        "ax.set_title('Does this building have an electrical meter box?', fontsize=16)\n",
        "\n",
        "# Set aspect ratio to equal to ensure it's a circle\n",
        "ax.set_aspect('equal')\n",
        "\n",
        "# Adjust label rotation and positioning for better visibility\n",
        "for text in ax.texts:\n",
        "    if text.get_text().endswith('%'):\n",
        "        text.set_fontsize(16)  # Increase font size for better visibility\n",
        "        text.set_position((text.get_position()[0] * 0.8, text.get_position()[1] * 0.8))  # Move labels inward\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jQQS3DaEneLr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "d2ac79e3-473a-4827-c608-85ac1729ecab"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define categories\n",
        "df_temp['Category'] = 'Other'\n",
        "df_temp.loc[df_temp['4.1 Does this building have an electrical meter box?'] == 'No', 'Category'] = 'No meter box'\n",
        "df_temp.loc[(df_temp['4.1 Does this building have an electrical meter box?'] == 'Yes') &\n",
        "            (df_temp['4.2 Is the meter box unlocked and accessible?'] == 'No'), 'Category'] = 'Inaccessible for inspection'\n",
        "\n",
        "# Use responses from '4.3 What is the condition of the meter box' for the remaining categories\n",
        "df_temp.loc[(df_temp['4.1 Does this building have an electrical meter box?'] == 'Yes') &\n",
        "            (df_temp['4.2 Is the meter box unlocked and accessible?'] == 'Yes'), 'Category'] = df_temp['4.3 What is the condition of the meter box']\n",
        "\n",
        "# Replace specific labels in the '4.3' categories\n",
        "df_temp['Category'] = df_temp['Category'].replace({\n",
        "    'In use, intact enclosure, no exposed or spliced wiring': 'Good working order',\n",
        "    'In use, broken/missing enclosure, no exposed or spliced wiring': 'In use with unsafe conditions'\n",
        "})\n",
        "\n",
        "# Count occurrences for each category\n",
        "category_counts = df_temp['Category'].value_counts()\n",
        "\n",
        "# Convert counts to percentages\n",
        "category_percentages = (category_counts / category_counts.sum()) * 100\n",
        "category_percentages = category_percentages.sort_values(ascending=True)  # Sort in ascending order for horizontal bar chart\n",
        "\n",
        "# Plot the horizontal bar chart\n",
        "plt.figure(figsize=(10, 6))\n",
        "bars = plt.barh(category_percentages.index, category_percentages, color='#4E79A7', edgecolor='black', alpha=0.8)\n",
        "\n",
        "# Add data labels\n",
        "for bar in bars:\n",
        "    plt.text(bar.get_width() + 1, bar.get_y() + bar.get_height() / 2,\n",
        "             f\"{bar.get_width():.0f}%\", va='center', ha='left', fontsize=12, color='black')\n",
        "\n",
        "# Add labels and title\n",
        "plt.title('Condition and accessibility of meter boxes', fontsize=16)\n",
        "plt.xlabel('Percentage of respondents', fontsize=14)\n",
        "plt.xticks(\n",
        "    ticks=range(0, 51, 10),  # Tick intervals of 10%\n",
        "    labels=[f\"{tick}%\" for tick in range(0, 51, 10)],\n",
        "    fontsize=12\n",
        ")\n",
        "plt.yticks(fontsize=12)  # Keep y-tick labels for categories readable\n",
        "\n",
        "# Bound x-axis at 50%\n",
        "plt.xlim(0, 50)\n",
        "\n",
        "# Remove the y-axis label and gridlines\n",
        "plt.ylabel(None)  # Remove the y-axis label\n",
        "plt.grid(False)  # Remove gridlines\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZZgynVQnW17O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "f71e7996-9e7b-4ca0-afe6-bde2b5a78bef"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Replace NaN with 'No Response' for clarity\n",
        "df_test = df_temp.fillna('No Response')\n",
        "\n",
        "# Group data to count occurrences of Meter Box and Circuit Breaker combinations\n",
        "simplified_flows = df_test.groupby(['4.1 Does this building have an electrical meter box?',\n",
        "                                    '5.1 Does this building have an electrical breaker panel?']).size().reset_index(name='Count')\n",
        "\n",
        "# Calculate percentages for each flow\n",
        "total_count = simplified_flows['Count'].sum()\n",
        "simplified_flows['Percentage'] = (simplified_flows['Count'] / total_count) * 100\n",
        "\n",
        "# Prepare Sankey diagram data\n",
        "sources = []\n",
        "targets = []\n",
        "values = []\n",
        "hovertext = []\n",
        "\n",
        "# Define flows with unique labels\n",
        "for _, row in simplified_flows.iterrows():\n",
        "    source_label = \"Meter box: Yes\" if row['4.1 Does this building have an electrical meter box?'] == 'Yes' else \"Meter box: No\"\n",
        "    target_label = \"Circuit breaker: Yes\" if row['5.1 Does this building have an electrical breaker panel?'] == 'Yes' else \"Circuit breaker: No\"\n",
        "    sources.append(source_label)\n",
        "    targets.append(target_label)\n",
        "    values.append(row['Count'])\n",
        "    hovertext.append(f\"{source_label} → {target_label}: {row['Count']} ({row['Percentage']:.2f}%)\")\n",
        "\n",
        "# Combine unique labels and add percentages to nodes\n",
        "unique_labels = list(set(sources + targets))\n",
        "label_percentages = {\n",
        "    label: f\"{label} ({simplified_flows[(simplified_flows['4.1 Does this building have an electrical meter box?'] == label.split(': ')[1]) | (simplified_flows['5.1 Does this building have an electrical breaker panel?'] == label.split(': ')[1])]['Percentage'].sum():.2f}%)\"\n",
        "    for label in unique_labels\n",
        "}\n",
        "labels_with_percentages = [label_percentages[label] for label in unique_labels]\n",
        "\n",
        "# Map labels to indices\n",
        "label_indices = {label: i for i, label in enumerate(unique_labels)}\n",
        "\n",
        "# Map sources and targets to indices\n",
        "source_indices = [label_indices[source] for source in sources]\n",
        "target_indices = [label_indices[target] for target in targets]\n",
        "\n",
        "# Define light colors for nodes and links\n",
        "node_colors = [\"#71b3ff\" if \"Yes\" in label else \"#e3555b\" for label in unique_labels]\n",
        "link_colors = [\"#71b3ff\" if \"Yes\" in source else \"#e3555b\" for source in sources]\n",
        "\n",
        "# Create the Sankey diagram\n",
        "fig = go.Figure(data=[go.Sankey(\n",
        "    node=dict(\n",
        "        pad=15,\n",
        "        thickness=30,\n",
        "        line=dict(color=\"black\", width=1.0),\n",
        "        label=labels_with_percentages,\n",
        "        color=node_colors\n",
        "    ),\n",
        "    link=dict(\n",
        "        source=source_indices,\n",
        "        target=target_indices,\n",
        "        value=values,\n",
        "        color=link_colors,\n",
        "        customdata=hovertext,\n",
        "        hovertemplate='%{customdata}<extra></extra>'\n",
        "    )\n",
        ")])\n",
        "\n",
        "# Update layout and display\n",
        "fig.update_layout(\n",
        "    title_text=\"Sankey Diagram: Meter Box and Circuit Breaker\",\n",
        "    font=dict(size=14),\n",
        "    hoverlabel=dict(font_size=14),\n",
        "    margin=dict(l=200, r=200, t=50, b=50)  # Adjusted margins for label visibility\n",
        ")\n",
        "\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2IMczBE1dNN-"
      },
      "source": [
        "## Socket testing results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lb6q1LC-dOpR"
      },
      "outputs": [],
      "source": [
        "df_temp = df_wiring_inspection['Socket_state']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oXHMvH_CdVRc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "outputId": "83ea60bd-c301-41ef-9109-8d6cfb9c04eb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Drop rows where the column has NaN values\n",
        "filtered_df = df_temp.dropna(subset=['7.7 Record the wiring status of socket ${n_sockets_repeat}.']).copy()\n",
        "\n",
        "# Categorize responses\n",
        "filtered_df.loc[:, 'Category'] = filtered_df['7.7 Record the wiring status of socket ${n_sockets_repeat}.'].replace({\n",
        "    'Open Ground': 'Open ground',\n",
        "    'Correct': 'Correct'\n",
        "})\n",
        "filtered_df.loc[~filtered_df['Category'].isin(['Open ground', 'Correct']), 'Category'] = 'Other'\n",
        "\n",
        "# Calculate weights for each unique _submission__id\n",
        "weights = filtered_df.groupby('_submission__id')['Category'].transform('count')\n",
        "filtered_df['Weighted Count'] = 1 / weights\n",
        "\n",
        "# Aggregate weighted counts by category\n",
        "weighted_counts = filtered_df.groupby('Category')['Weighted Count'].sum()\n",
        "\n",
        "# Convert counts to percentages\n",
        "category_percentages = (weighted_counts / weighted_counts.sum()) * 100\n",
        "category_percentages = category_percentages.sort_values(ascending=True)  # Sort in ascending order for horizontal bar chart\n",
        "\n",
        "# Plot the horizontal bar chart\n",
        "plt.figure(figsize=(10, 3))\n",
        "bars = plt.barh(category_percentages.index, category_percentages, color='#71b3ff', edgecolor='black', alpha=0.8)\n",
        "\n",
        "# Add data labels\n",
        "for bar in bars:\n",
        "    plt.text(bar.get_width() + 1, bar.get_y() + bar.get_height() / 2,\n",
        "             f\"{bar.get_width():.0f}%\", va='center', ha='left', fontsize=12, color='black')\n",
        "\n",
        "# Add labels and title\n",
        "#plt.title('Wiring status of sockets (weighted by submission)', fontsize=16)\n",
        "plt.xlabel('Percentage of respondents', fontsize=14)\n",
        "plt.xticks(\n",
        "    ticks=range(0, 101, 10),  # Adjust tick intervals as needed\n",
        "    labels=[f\"{tick}%\" for tick in range(0, 101, 10)],\n",
        "    fontsize=12\n",
        ")\n",
        "plt.yticks(fontsize=12)  # Keep y-tick labels for categories readable\n",
        "\n",
        "# Bound x-axis at 100%\n",
        "plt.xlim(0, 100)\n",
        "\n",
        "# Remove the y-axis label and gridlines\n",
        "plt.ylabel(None)  # Remove the y-axis label\n",
        "plt.grid(False)  # Remove gridlines\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(fig_path + \"Socket Testing Results.png\", dpi=500)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "alWLHJmQgnCm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7248f4ee-3236-4da6-c91a-b02f8bc4a061"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of households surveyed: 78\n",
            "Number of sockets surveyed: 96\n"
          ]
        }
      ],
      "source": [
        "# Calculate the number of households surveyed\n",
        "num_households = filtered_df['_submission__id'].nunique()\n",
        "\n",
        "# Calculate the number of sockets surveyed\n",
        "num_sockets = filtered_df.shape[0]\n",
        "\n",
        "print(f\"Number of households surveyed: {num_households}\")\n",
        "print(f\"Number of sockets surveyed: {num_sockets}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yOgFTLhydNfF"
      },
      "source": [
        "## Conductor conditions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G_mQHe5_u-92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "outputId": "71d5d28f-3d5c-4095-b90b-7be0b8d99c8e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x200 with 1 Axes>"
            ],
            "image/png": 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YWVlJY7UvXryIly9fSrOTe3l5SbcgnDp1CllZWVKh/j5yuRwBAQEFjiMgIEBprHe9evUAALdu3QIAnD17Fk+fPkX//v2lghsAunXrJg3yf5/t27cjOzsbHTt2xJMnT6QfW1tblC5dWmmWdltbW/z88884dOgQ6tWrh6ioKPzyyy8wNTVVWW/r1q2lghsAatasiVq1akm3oT948ABRUVHo3bu3VMwBOV8WfPvtt1I/ADA3N0dkZCTu37+faw5RUVGIjY1F165d8fTpUymHly9fomHDhjh+/Diys7ORlZWFAwcOoHXr1kqFTvny5dGkSZN899XZs2fx+PFjDBkyRCq4AaBZs2YoV64c9u7dq/KawYMH57ve96lQoYJUcAOQ3uANGjRQykHRrjgv3hYYGCj9v+Kq6Zs3b3D48GGlfu3atVMquAtzjBS+++47pd+HDh0KAFLfwpxvQMHfL40aNYK9vT02bdoktV2+fBmXLl1C9+7d3/tac3NzvHz5EocOHcp3OwMGDJAKbiDn+Oro6OS6L/KSmJiI48ePo0+fPipfBii+MBJCYNu2bWjRogWEEEr7qkmTJkhOTpaGV/z111+ws7ND+/btpfUYGhpiwIAB741j5syZMDMzk34cHR0LnAMRERHRl2DZsmWoWrWqyoXIli1b4tWrV9LwTHd3d1y+fBmXL19GWFgY7t+/j/79++PJkycoU6ZMnutPSUnBxIkTMWvWLBgZGWHz5s1o3749WrdujVatWqF9+/ZKn0+/BoUqumUyGby8vKSx2+Hh4ShevDhKlSoFQLnoVvy3IEV3iRIlCjVh2rsfyhWFtGIcaVxcHABIcSno6Oio3Pqbm9jYWAghULp0aVhbWyv9XL16FY8fP1bq37lzZzRr1gynT59G//790bBhw1zXW7p0aZW2MmXKSGOWFXErxlC8rXz58lLRDABz5szB5cuX4ejoiJo1ayI4OFipuIyNjQWQ89y8d3NYs2YNXr9+jeTkZCQmJiItLS3X2HKL413vi7lcuXLScgUdHZ0C3d7/Pu8ef8Xz/t4tkBTt744v1tLSgqurq1Kb4h+Od58J7uLiovR7YY6Rwrv71s3NDVpaWtK2Cnu+FfT9oqWlhW7dumHnzp3SZGubNm2Cvr6+dPU3L0OGDEGZMmXg7+8PBwcH9OnTB/v378+177v5GRsbw87OrlDPV1ecuxUrVsyzT2JiIpKSkrBq1SqV/aT4EkKxr+Li4lCqVCmV29vzO6cnTJiA5ORk6efdMfVEREREX7pHjx4hKytLpT0jIwNAznBQBZlMBnd3d9StWxeWlpY4cuQIsrOz0ahRozzXP3XqVLi4uEhDGe/fvw97e3tpub29vcrcT5quULeXAzlF9J49exAdHS2N51bw8vLC2LFjkZCQgBMnTsDe3l6luMnNu7Pi5Sev213fngjrv8jOzoZMJsO+ffty3da7t1M8ffoUZ8+eBQD8+++/yM7OVvuU+B07dkS9evWwY8cOHDx4EHPnzsXs2bOxfft2+Pv7SxPazZ07F1WqVMl1HcbGxp98Yim5XP6f901ex18d50Vhz82CeLcQLOz5VpiYevbsiblz52Lnzp3o0qULfvvtNzRv3lz6QiIvxYsXR1RUFA4cOIB9+/Zh37590vMb3719/lNRnNPdu3dHr169cu2jmE/iQ8nlcsjl8v+0DiIiIqLPWZkyZXDw4EHExMQoXbHevHkztLS08vw8lZaWhkmTJsHOzg5dunTJtU9MTAyWLl2K48ePS595bWxscO3aNanP1atXYWtr+xEz+vx9UNEN5IzrDg8PlyYuAgBPT0/I5XIcPXoUkZGRKhOJfSqK5zrfuHFDaaxAZmYm7ty5k+8Hczc3Nwgh4OLi8t5bJxS+++47pKamYubMmZgwYQIWLlyIUaNGqfRTXH1+W0xMjHT1XRH39evXVfpdu3YNxYoVg5GRkdRmZ2eHIUOGYMiQIXj8+DGqVauGkJAQ+Pv7w83NDQBgamr63m+iFLNE5xZbbnG86+2YGzRooPL6z/EZ29nZ2bh165bSsY2JiQGAfO+EKOwxAnKO+9tXzG/cuIHs7GxpW4U93wqjYsWKqFq1KjZt2gQHBwfcvXsXS5YsKdBr9fT00KJFC7Ro0QLZ2dkYMmQIVq5ciUmTJindRRIbG6v0Pnvx4gUePHhQqPe/4su53GbzV7C2toaJiQmysrLee04DOcfp8uXLEEIofclRkHOaiIiISJONHTsW+/btQ7169RAYGAgrKyv8+eef2LdvH/r16yddle7YsSPs7e1RoUIFpKSk4JdffsGtW7ewd+9emJiY5LrukSNHolOnTtKEzQDQvn17tGrVSpqjaM+ePfjzzz/Vn+hnpNCXHKtXrw59fX1s2rQJCQkJSle65XI5qlWrhp9//hkvX74s0K3l6lC9enVYWVlh9erVSrdHbNq0Kd9HGQE5Ewloa2tjypQpKldJhRB4+vSp9Psff/yBLVu2YNasWRg/fjw6d+6MH3/8USri3rZz506lWylOnz6NyMhI+Pv7A8gpoqtUqYINGzYgKSlJ6nf58mUcPHhQKmKysrKQnJystO7ixYvD3t5eunLt6ekJNzc3zJs3Dy9evFCJJTExEUDO1eEmTZpg586duHv3rrT86tWrOHDgQL77qnr16ihevDhWrFihdNV83759uHr1Kpo1a5bvOorC0qVLpf8XQmDp0qXQ1dXNc2iAQkGP0dt+/vlnpd8VRa/iuBfmfPsQPXr0wMGDB7Fw4UJYWVlJ232fd7f59ree794dsWrVKul2JCDnyQGZmZkF2o6CtbU1vL298csvvyidh8D/3amgra2Ndu3aYdu2bbkW54pzGgCaNm2K+/fv448//pDaXr16hVWrVhU4JiIiIiJN5O3tjZMnT8LT0xPLli3DiBEjcPPmTYSEhGD58uVSv+rVq+PAgQMYPnw4ZsyYgdKlSyMiIiLPz8t//fUXjh8/rjQJLgA0b94cISEh2LBhA9avX4+ZM2cW6nOiJij0lW49PT3UqFEDYWFhkMvl8PT0VFru5eUlzahdVEW3np4egoODMXToUDRo0AAdO3bEnTt3sH79eri5ueX7GCM3NzdMnz4dEyZMkB41ZmJigtu3b2PHjh0YMGAAxowZg8ePH2Pw4MHw9fWVJuZaunQpjhw5gt69e+PEiRNKt1KXKlUKdevWxeDBg/H69WupCHp71vG5c+fC398ftWvXRt++faXHUZmZmSE4OBhAzjO6HRwc0L59e1SuXBnGxsY4fPgwzpw5I+17LS0trFmzBv7+/nB3d0dAQABKlCiBhIQEHDlyBKamptizZw8AYMqUKdi/fz/q1auHIUOGIDMzE0uWLIG7uzsuXbr03n2lq6uL2bNnIyAgAPXr10eXLl2kR4Y5Oztj5MiRhT5+6qavr4/9+/ejV69eqFWrFvbt24e9e/fihx9+UJo0LS8FOUZvu337Nlq2bAk/Pz+cOnUKv/76K7p27SrNpF3Q8+1Dde3aFePGjcOOHTswePBgpUnP8tKvXz88e/YMDRo0gIODA+Li4rBkyRJUqVIF5cuXV+r75s0bNGzYUHo827Jly1C3bt1CzVwOAIsXL0bdunVRrVo1DBgwAC4uLrhz5w727t2LqKgoADkzmR85cgS1atVC//79UaFCBTx79gznz5/H4cOH8ezZMwA5M5UvXboUPXv2xLlz52BnZ4fQ0NAPekQdERERkaapWbNmvpPejhs3TqlOyU/Tpk2Rmpqa67Lx48dj/PjxhYpRkxS66AZyiumwsDDpdvK31alTB/Pnz4eJiUm+j+dRp8DAQAghMH/+fIwZMwaVK1fG7t27MWzYMKVZtvMyfvx4lClTRumRV46OjmjcuLFUTCiK53Xr1kmFvJWVFVatWoVWrVph3rx5Sidqz549oaWlhYULF+Lx48eoWbMmli5dCjs7O6lPo0aNpOdGT548Gbq6uqhfvz5mz54t3aJsaGiIIUOG4ODBg9LM16VKlcKyZcuUZgb38fHBqVOnMG3aNCxduhQvXryAra0tatWqpfS4KQ8PDxw4cACjRo3C5MmT4eDggClTpuDBgwf5Ft0A0Lt3bxgaGmLWrFn4/vvvYWRkhDZt2mD27NlKz5f+XGhra2P//v0YPHgwxo4dCxMTE2l/F0RBjtHbtmzZgsmTJ2P8+PHQ0dFBYGAg5s6dq9SnIOfbh7KxsUHjxo3x119/oUePHgV6Tffu3bFq1SosW7YMSUlJsLW1RadOnRAcHKwyJn/p0qXYtGkTJk+ejIyMDHTp0gWLFy8u1DO6AaBy5cqIiIjApEmTsHz5cqSnp8PJyQkdO3ZUyuX06dOYOnUqtm/fjmXLlsHKygru7u7S88SBnPfI33//jaFDh2LJkiUwNDREt27d4O/vr/T4NCIiIiIidZOJjzX72BcgOzsb1tbWaNu2LVavXv3Jtnvnzh24uLhg7ty5/+mKJf13vXv3xh9//JHrLfcfW3BwMKZMmYLExESlZ38XhTZt2iA6Oho3btz4aOtcv349AgICcObMGVSvXv2jrfdzkpKSAjMzMwxfdQYly2lmjkRERKRZHsVdw+453bFj668oV65cUYdTpBSf5ZKTk3N9pPOnot4ptotQenq6yvjYjRs34tmzZ/Dx8SmaoIiKwIMHD7B3794CX+UmIiIiIqKP54NuL/8SREREYOTIkejQoQOsrKxw/vx5rF27FhUrVsz3GcVEmuD27dsIDw/HmjVroKurqzSkgIiIiIiIPg2NLbqdnZ3h6OiIxYsX49mzZ7C0tETPnj0xa9Ys6OnpFXV4RGp37NgxBAQEoGTJktiwYcNX9zxEIiIiIqLPwVc1ppuIqDA4ppuIiIi+NBzT/X84ppuIiIiIiIhIw7HoJiIiIiIiIlITFt1EREREREREasKim4iIiIiIiEhNWHQTERERERERqQmLbiIiIiIiIiI1YdFNREREREREpCYsuomIiIiIiIjUhEU3ERERERERkZqw6CYiIiIiIiJSExbdRERERERERGqiU9QBEBF97p4/jIPcwLiowyAiIiLK17MHt4s6BHoHi24ionyc+t90aGtrF3UYRERERAVibKgPc3Pzog6D/j8W3URE+fhtw2oYG/NKNxEREX0ZzM3NYWtrW9Rh0P/HopuIKB9lypSBqalpUYdBRERERF8gTqRGREREREREpCYsuomIiIiIiIjUhEU3ERERERERkZqw6CYiIiIiIiJSExbdRERERERERGrCopuIiIiIiIhITVh0ExEREREREakJi24iIiIiIiIiNdEp6gCIiD53MTExMDY2LuowPgvm5uawtbUt6jCIiIiIvhgsuomI8tG1V39oa2sXdRifBWNDfezZ+QcLbyIiIqICYtFNRJSP2p1/hK2Le1GHUeSePbiNE6GTkJSUxKKbiIiIqIBYdBMR5cPC1gk2TuWKOgwiIiIi+gJxIjUiIiIiIiIiNWHRTURERERERKQmLLqJiIiIiIiI1IRFNxEREREREZGasOgmIiIiIiIiUhMW3URERERERERqwqKbiIiIiIiISE1YdBMRERERERGpCYtuIiIiIiIiIjVh0U1ERERERESkJiy6iYiIiIiIiNSERTcRERERERGRmrDoJiIiIiIiIlITFt1ERPTRvHjxAkFBQfDz84OlpSVkMhnWr1+v0u/06dMYMmQIPD09oaurC5lMluv6Xr9+jaFDh8La2hoODg6YPn26Sp979+7B2NgY4eHhHzsdIiIiov+MRTfl6ejRo5DJZDh69GiB+vv4+MDHx0etMeXF2dkZvXv3LpJtfypfYo7r16+HTCbDnTt3pLbCnCe9e/eGs7OzWmIj9Xjy5AmmTp2Kq1evonLlynn2++uvv7BmzRrIZDK4urrm2W/u3LnYuHEjxo4di4CAAEydOhWbN29W6jN27Fi0bNkSderU+Wh5EBEREX0sLLo11NatWyGTybBjxw6VZZUrV4ZMJsORI0dUlpUsWRJeXl6fIkQiAMD9+/cRHByMqKioog6FPgI7Ozs8ePAAcXFxmDt3bp79Bg8ejOTkZJw9exbffvttnv3+/PNPjB49GuPGjcO0adPQtWtX7N69W1p+4sQJ7NmzB3PmzPmoeRARERF9LDpFHQCpR926dQHkfCBt06aN1J6SkoLLly9DR0cH4eHh8PX1lZbFx8cjPj4enTt3BgB4e3sjLS0Nenp6BdrmwYMHP2IGhXP9+nVoafE7pC/Bu+fJ/fv3MWXKFDg7O6NKlSpKy1avXo3s7OxPGB39V3K5HLa2tvn2s7GxKdD60tLSYGFhIf1uaWmJmzdvAgCys7MxfPhwjBs3Dg4ODh8WMBEREZGasUrRUPb29nBxccGJEyeU2k+dOgUhBDp06KCyTPG7omDX0tKCvr5+vsXsq1evAAB6enoFLtA/NrlcDl1d3SLZNhVOYc4TXV1dyOVyNUdEn7MaNWpg1apViI6OxqlTp7B582bUrFkTALB27Vo8efIEY8eOLeIoiYiIiPLGoluD1a1bFxcuXEBaWprUFh4eDnd3d/j7+yMiIkLpKmJ4eDhkMpk0LjK3Md0+Pj6oWLEizp07B29vbxgaGuKHH36Qlr09Vlfx+q1btyIkJAQODg7Q19dHw4YNcePGDZV4f/75Z7i6usLAwAA1a9ZEWFhYgcf/vjveWTGWODw8HKNGjYK1tTWMjIzQpk0bJCYmFmj//f7776hQoQL09fVRsWJF7NixI9cxxi9fvsTo0aPh6OgIuVyOsmXLYt68eRBCKPXLzMzEtGnT4ObmBrlcDmdnZ/zwww94/fq1Uj8hBKZPnw4HBwcYGhrC19cXV65cKVDMQM7Vv0WLFqFSpUrQ19eHtbU1/Pz8cPbs2ULH4uzsjObNm+PEiROoWbMm9PX14erqio0bN6ps98qVK2jQoAEMDAykCa9yu0r99jE9evQoatSoAQAICAiATCZTmnjrv+xvmUyGwMBA7Ny5ExUrVoRcLoe7uzv2799f4H1JRS84OBhCCHh4eMDLywulS5fG8OHDkZycjIkTJ2LOnDkwMDAo6jCJiIiI8sSiW4PVrVsXGRkZiIyMlNrCw8Ph5eUFLy8vJCcn4/Lly0rLypUrBysrq/eu9+nTp/D390eVKlWwcOFCpVvUczNr1izs2LEDY8aMwYQJExAREYFu3bop9Vm+fDkCAwPh4OCAOXPmoF69emjdujXu3bv3AZn/n6FDh+LixYsICgrC4MGDsWfPHgQGBub7ur1796JTp07Q1dXFzJkz0bZtW/Tt2xfnzp1T6ieEQMuWLbFgwQL4+fnhp59+QtmyZTF27FiMGjVKqW+/fv0wefJkVKtWDQsWLED9+vUxc+ZM6XZ+hcmTJ2PSpEmoXLky5s6dC1dXVzRu3BgvX74sUM59+/bFiBEj4OjoiNmzZ2P8+PHQ19dHREREoWMBgBs3bqB9+/b49ttvMX/+fFhYWKB3795KXwQ8fPgQvr6+iIqKwvjx4zFixAhs3LgRixYtem+s5cuXx9SpUwEAAwYMQGhoKEJDQ+Ht7Z1r/8LsbyDn7o0hQ4agc+fOmDNnDtLT09GuXTs8ffq0QPuSip6DgwMuXLiACxcu4MqVKzh69CiMjY0xZcoUlC1bFp06dcKJEydQq1YtODo6YtiwYXjz5k1Rh01EREQk4ZhuDfb2uG4fHx9kZmYiMjISvXr1gpubG2xsbHDixAl4eHggNTUV0dHR6NOnT77rffjwIVasWIGBAwcWKI709HRERUVJtxRbWFhg+PDhuHz5MipWrIg3b95g0qRJqFGjBv755x/o6OSclh4eHujdu/d/GqtpZWWFgwcPSo8jys7OxuLFi5GcnAwzM7M8XzdhwgSUKFEC4eHhMDY2BgA0bNgQPj4+cHJykvrt3r0b//zzD6ZPn46JEycCAL777jt06NABixYtQmBgINzc3HDx4kVs2LAB/fr1w+rVqwEAQ4YMQfHixTFv3jwcOXIEvr6+SExMxJw5c9CsWTPs2bNHinvixImYMWNGvvkeOXIE69evx7Bhw5QK3tGjR0tXggsai8L169dx/Phx1KtXDwDQsWNHODo6Yt26dZg3bx4AYPbs2UhMTERkZKR062+vXr1QunTp98ZrY2MDf39/TJ48GbVr10b37t3f27+g+1vh6tWr+Pfff6U2X19fVK5cGZs3b871y5fXr18rXe1PSUl5bzz0aejq6iqN97927RqWLVuGkydP4tmzZ2jWrBnGjx8PX19fBAQEICQkBFOmTCm6gImIiIjewivdGqx8+fKwsrKSxmpfvHgRL1++lGYn9/Lykp5re+rUKWRlZUmF+vvI5XIEBAQUOI6AgAClMbyK4u3WrVsAgLNnz+Lp06fo37+/VHADQLdu3ZQmUPoQAwYMUHr+b7169ZCVlYW4uLg8X3P//n1ER0ejZ8+eUsENAPXr10elSpWU+v7111/Q1tbGsGHDlNoVRe6+ffukfgBUrsaOHj0aQM6VdQA4fPgw3rx5g6FDhyrFPWLEiALlu23bNshkMgQFBaksU6yvoLEoVKhQQTpmAGBtbY2yZctKx0+xzm+++UYquBX93r2j4b8q6P5WaNSokVIR7uHhAVNTU6XY3zZz5kyYmZlJP46Ojh81fvo4Ro4cie7du6NatWrYu3cvLC0tMWHCBHzzzTcYN24cNm3aVNQhEhEREUlYdGswmUwGLy8vaex2eHg4ihcvjlKlSgFQLroV/y1I0V2iRIlCTZhWsmRJpd8VhfTz588BQCqAFXEp6Ojo/OdnNOe37dzkFU9ubXFxcbC3t4eJiYlSe/ny5ZXWFRcXBy0tLZXX29rawtzcXKkfAJUrxNbW1gX6AuLmzZuwt7eHpaXle/MrSCwK7+5DIGc/vr0P4+Licr2qXbZs2XxjLoyC7m+FgsT+tgkTJiA5OVn6iY+P/0iR08fy559/4uTJk9KdH/fv34ednZ203N7eHgkJCUUVHhEREZEKFt0arm7dukhOTkZ0dLQ0nlvBy8sLcXFxSEhIwIkTJ2Bvbw9XV9d811nYSYu0tbVzbX934it1KMpt5+btq9dFraCxfG77sDAKG7tcLoepqanSD30+3rx5g1GjRuHHH39E8eLFAeQMUbhx4wYyMzMB5AwpKMgjy4iIiIg+FY7p1nBvj+sODw9Xuk3Z09MTcrkcR48eRWRkJJo2bVokMSrGSN+4cUNpLHFmZibu3LkDDw+PIovnXe+2OTk54fDhw0hNTVW6+nrt2jWldTk5OSE7OxuxsbHSVVkAePToEZKSkpT6AUBsbKzSFyCJiYnvvTqv4ObmhgMHDuDZs2d5Xu0uaCyF4eTkhNjYWJX269ev5/vawnwRUdD9TUVr6dKlSEpKwv379wEAe/bskSZFHDp0KMzMzBAXF4fQ0FAAkGbWnz59OoCc49ijRw+V9SrmKRg+fLjU1rRpU3z33Xfo2rUrvLy8MG3aNPTr1099yREREREVEq90a7jq1atDX18fmzZtQkJCgtKVbrlcjmrVquHnn3/Gy5cvC3RrubpitLKywurVq6WrVQCwadOmAhWaH5u9vT0qVqyIjRs34sWLF1L7sWPHEB0drdS3adOmyMrKwtKlS5XaFyxYAJlMBn9/f6kfACxcuFCp308//QQAaNasGYCcMci6urpYsmSJ0tXYd1+Xl3bt2kEIkeskUor1FTSWwmjatCkiIiJw+vRpqS0xMbFAY2uNjIwAAElJSQXaTkH2NxWtefPmYdKkSVi+fDkAYPv27Zg0aRImTZokvadv374ttSmesKD4fe3atSrrfPToEaZNm4b58+crDW8pXrw4tm3bhujoaEyZMgXNmzfPdU4DIiIioqLCK90aTk9PDzVq1EBYWBjkcjk8PT2Vlnt5eWH+/PkACjaeWx309PQQHByMoUOHokGDBujYsSPu3LmD9evXw83NrUhuyZ4xYwZatWqFOnXqICAgAM+fP8fSpUtRsWJFpUK8RYsW8PX1xcSJE3Hnzh1UrlwZBw8exK5duzBixAhpEq/KlSujV69eWLVqFZKSklC/fn2cPn0aGzZsQOvWraUr/NbW1hgzZgxmzpyJ5s2bo2nTprhw4QL27duHYsWK5Ru3r68vevTogcWLFyM2NhZ+fn7Izs5GWFgYfH19ERgYWOBYCmPcuHEIDQ2Fn58fhg8fDiMjI6xatQpOTk64dOnSe1/r5uYGc3NzrFixAiYmJjAyMkKtWrXg4uKi0reg+5uK1p07d/Lt4+PjU6ghCjY2NnnOJu/n5wc/P78Cr4uIiIjoU+KV7q+AophW3E7+tjp16gAATExMULly5U8em0JgYCAWL16Mu3fvYsyYMQgLC8Pu3bthbm4OfX39Tx5PixYtsHnzZrx58wbjx4/H9u3bsX79epQtW1YpHi0tLezevRsjRozAn3/+iREjRuDff//F3LlzpSvHCmvWrMGUKVNw5swZjBgxAv/88w8mTJiA//3vf0r9pk+fjilTpuDChQsYO3Ysbt68iYMHD0pXhPOzbt06zJ07F7dv38bYsWMxY8YMpKWlKd3lUNBYCsrOzg5HjhyBh4cHZs2ahYULF6Jnz55KtwHnRVdXFxs2bIC2tjYGDRqELl264NixY7n2Lcz+JiIiIiL6HMjElzAbEn2VsrOzYW1tjbZt20rPky5qVapUgbW1NQ4dOlTUodAnkJKSAjMzMwxfdQYly1Uv6nCK3KO4a9g9pzt2bP0V5cqVK+pwiIiIiN5L8VkuOTm5SCfI5ZVu+iykp6er3Gq6ceNGPHv2DD4+Pp88noyMDKXx5QBw9OhRXLx4sUjiISIiIiKiLxPHdNNnISIiAiNHjkSHDh1gZWWF8+fPY+3atahYsSI6dOjwyeNJSEhAo0aN0L17d9jb2+PatWtYsWIFbG1tMWjQoE8eDxERERERfZlYdNNnwdnZGY6Ojli8eLH0uKuePXti1qxZSjMVfyoWFhbw9PTEmjVrkJiYCCMjIzRr1gyzZs2ClZXVJ4+HiIiIiIi+TCy66bPg7OyM3bt3F3UYEjMzM2zZsqWowyAiIiIioi8cx3QTERERERERqQmLbiIiIiIiIiI1YdFNREREREREpCYsuomIiIiIiIjUhEU3ERERERERkZqw6CYiIiIiIiJSExbdRERERERERGrCopuIiIiIiIhITVh0ExEREREREakJi24iIiIiIiIiNdEp6gCIiD53zx/GQW5gXNRhFLlnD24XdQhEREREXxwW3URE+Tj1v+nQ1tYu6jA+C8aG+jA3Ny/qMIiIiIi+GCy6iYjy8duG1TA25pVuADA3N4etrW1Rh0FERET0xWDRTUSUjzJlysDU1LSowyAiIiKiLxAnUiMiIiIiIiJSExbdRERERERERGrC28uJiPIghAAApKSkFHEkRERERFRYis9wis90RYVFNxFRHp4+fQoAcHR0LOJIiIiIiOhDPX36FGZmZkW2fRbdRER5sLS0BADcvXu3SP+hLgopKSlwdHREfHz8VzeJHHP/+nL/WvMGmPvXmPvXmjfA3L/G3JOTk1GyZEnpM11RYdFNRJQHLa2caS/MzMy+qj9QbzM1NWXuX6GvNfevNW+AuX+NuX+teQPM/WvMXfGZrsi2X6RbJyIiIiIiItJgLLqJiIiIiIiI1IRFNxFRHuRyOYKCgiCXy4s6lE+OuTP3r8nXmjfA3L/G3L/WvAHm/jXm/rnkLRNFPX86ERERERERkYbilW4iIiIiIiIiNWHRTURERERERKQmLLqJiIiIiIiI1IRFNxF9lV6/fo3vv/8e9vb2MDAwQK1atXDo0CGlPitXroSLiwssLS3Ro0cPpKSkKC3Pzs5G1apVMWPGjE8ZeoG9ePECQUFB8PPzg6WlJWQyGdavX59r36tXr8LPzw/GxsZSvomJiUp9kpKS0K1bN1hYWMDV1RVr165VWc/Zs2dhaGiI27dvqyOlAjlz5gwCAwPh7u4OIyMjlCxZEh07dkRMTIxKX03K+8qVK+jQoQNcXV1haGiIYsWKwdvbG3v27FHpq0l55yUkJAQymQwVK1ZUWXby5EnUrVsXhoaGsLW1xbBhw/DixQulPgkJCWjWrBlMTU1RoUKFXPfj9u3bUbx4cSQnJ6stj/wcPXoUMpks15+IiAilvpqUt8L58+fRsmVLWFpawtDQEBUrVsTixYuV+mha3r17987zmMtkMiQkJEh9NS13AIiNjUXnzp3h4OAAQ0NDlCtXDlOnTsWrV6+U+mla7ufOnYOfnx9MTU1hYmKCxo0bIyoqKte+X3LuH/uzC5DzeW3OnDlwcXGBvr4+PDw8sHnzZpV+O3fuRLly5WBmZoYWLVrg/v37Kn1atmyJAQMGFD4xQUT0FercubPQ0dERY8aMEStXrhS1a9cWOjo6IiwsTAghRFhYmJDJZGL48OFi0aJFwtbWVgwYMEBpHStWrBAuLi4iPT29KFLI1+3btwUAUbJkSeHj4yMAiHXr1qn0i4+PF8WKFRNubm5i0aJFIiQkRFhYWIjKlSuL169fS/369u0r7O3txaJFi8TQoUOFTCYT4eHh0vLs7GxRu3ZtMWHChE+RXp7atWsnbG1txdChQ8Xq1avFtGnThI2NjTAyMhLR0dFSP03Le+/evaJJkyYiODhYrFq1SixcuFDUq1dPABArV66U+mla3rmJj48XhoaGwsjISLi7uystu3DhgtDX1xdVq1YVy5cvFxMnThRyuVz4+fkp9WvYsKEoV66cWLZsmejWrZuQy+Xi9u3b0vK0tDTh4uKitG+LwpEjRwQAMWzYMBEaGqr0k5iYKPXTtLyFEOLAgQNCT09P1KpVS/z0009i1apV4vvvvxdjx46V+mhi3idPnlQ51hs3bhSGhoaiQoUKUj9NzP3u3bvC3NxcODk5iZkzZ4qVK1eK3r17CwCiZcuWUj9Ny/3cuXNCX19flC5dWsybN0/MmTNHODs7C1NTU3Ht2jWlvl967h/7s4sQQowfP14AEP379xerVq0SzZo1EwDE5s2bpT43b94Uenp6okePHmLZsmWiTJkyonHjxkrr2b9/vzAzMxOPHz8udF4suonoqxMZGSkAiLlz50ptaWlpws3NTdSuXVsIIcT3338vfH19peXr1q0Ttra20u/Pnz8XxYoVE9u2bft0gRdSenq6ePDggRBCiDNnzuT5h2vw4MHCwMBAxMXFSW2HDh1SKdZsbGzEhg0bpN/r168vxo8fL/0eGhoq7O3tRWpqqhqyKbjw8HCVP7gxMTFCLpeLbt26SW2alnduMjMzReXKlUXZsmWltq8h706dOokGDRqI+vXrqxTd/v7+ws7OTiQnJ0ttq1evFgDEgQMHhBBCvHr1SshkMnHs2DEhRM4XDC4uLmLFihXSa6ZNmyaqVKkisrKyPkFGeVMU3b///vt7+2la3snJycLGxka0adPmvbFoWt55CQsLEwBESEiI1KaJuYeEhAgA4vLly0rtPXv2FADEs2fPhBCal3vTpk2FhYWFePLkidR2//59YWxsLNq2bavU90vP/WN/drl3757Q1dUV3333ndSWnZ0t6tWrJxwcHERmZqYQQojly5cLV1dXkZ2dLYTI+bdVJpOJtLQ0IYQQGRkZonz58mL+/PkflBdvLyeir84ff/wBbW1tpduD9PX10bdvX5w6dQrx8fFIS0uDhYWFtNzS0lLp1rXg4GBUqlQJbdu2/aSxF4ZcLoetrW2+/bZt24bmzZujZMmSUlujRo1QpkwZbN26VWp73z55+fIlxo8fj5kzZ8LY2PgjZlF4Xl5e0NPTU2orXbo03N3dcfXqValN0/LOjba2NhwdHZGUlCS1aXrex48fxx9//IGFCxeqLEtJScGhQ4fQvXt3mJqaSu09e/aEsbGxlH96ejqEEFL+MpkM5ubmUv4JCQmYNWsWFi1aBC2tz+ejVGpqKjIzM1XaNTHv3377DY8ePUJISAi0tLTw8uVLZGdnK/XRxLzz8ttvv0Emk6Fr164ANDd3xTAvGxsbpXY7OztoaWlBT09PI3MPCwtDo0aNYGVlJbXZ2dmhfv36+PPPP6VbxzUh94/92WXXrl3IyMjAkCFDpDaZTIbBgwfj3r17OHXqFICcv3nm5uaQyWQAcv7mCSGQlpYGAFi6dCmysrIwdOjQD8qr6N89RESf2IULF1CmTBmlP0gAULNmTQBAVFQUatSogf379+PgwYOIjY3F/PnzpeX//vsvVqxYkeuH+i9NQkICHj9+jOrVq6ssq1mzJi5cuCD9XqNGDfz000+IjY3FgQMHsH//fmmfzJgxAyVKlECPHj0+WeyFIYTAo0ePUKxYMQCanffLly/x5MkT3Lx5EwsWLMC+ffvQsGFDAJqdNwDpA1G/fv1QqVIlleXR0dHIzMxUyV9PTw9VqlSR8rewsICbmxtmzJiB27dvY9OmTYiKipLyHzduHPz9/eHt7a3+pAooICAApqam0NfXh6+vL86ePSst08S8Dx8+DFNTUyQkJKBs2bIwNjaGqakpBg8ejPT0dACamXduMjIysHXrVnh5ecHZ2RmA5ubu4+MDAOjbty+ioqIQHx+PLVu2YPny5Rg2bBiMjIw0MvfXr1/DwMBApd3Q0BBv3rzB5cuXAWjucX9XYf6WXbhwAUZGRihfvrxKP8VyIOdv3oULF7B582bcvn0bISEhKFWqFCwsLJCYmIgpU6bgp59+gq6u7ocF/UHXx4mIvmDu7u6iQYMGKu1XrlwRAMSKFStEZmamaNu2rQAgAAhHR0dx6dIlIYQQjRs3FoMGDfrUYf8ned2ipWjfuHGjymvGjh0rAEhj1i9duiQcHBykfdKuXTuRlZUlbt26JQwMDMSpU6c+RSofJDQ0VAAQa9euFUJodt4DBw6UYtXS0hLt27eXbrnU5LyFEGLp0qVK4+3evb38999/FwDE8ePHVV7boUMHpSEkf//9t7CwsJDyHzFihBAiZ/iCgYGBuHPnjpqzKZjw8HDRrl07sXbtWrFr1y4xc+ZMYWVlJfT19cX58+eFEJqZt4eHhzA0NBSGhoZi6NChYtu2bWLo0KECgOjcubMQQjPzzs2ePXsEALFs2TKpTZNznzZtmjAwMJDiBSAmTpwoLdfE3CtVqiTKlCkj3QothBCvX78WJUuWFADEH3/8IYTQvNw/xmeXZs2aCVdXV5V+L1++FACUhk0NGzZM2h+Wlpbin3/+EUII0b9/f5Ux8YXFopuIvjqurq7C399fpf3mzZsCgFiwYIHUFhsbK86ePSuN6dm1a5cwNzcXiYmJ4t69e6J58+bCzs5ONG/eXCQkJHyqFAotrz9cx48fFwDEli1bVF4zadIkAUA8f/5caktLSxNnzpwRsbGxUlubNm1E9+7dhRBCbNu2TXh4eAhnZ2cxZcoUaWxUUbp69aowNTUVtWvXlj6waHLeV69eFYcOHRIbNmwQzZo1E23atBEPHz4UQmh23k+ePBGWlpZi3rx5Utu7RffGjRsFABEZGany+h49eggzMzOlttTUVBERESHu3r0rhBAiKytLeHp6ih9//FEIIcSyZctE2bJlRZkyZcTy5cvVkNWHiY2NFQYGBqJJkyZCCM3M29XVVQBQ+QJU8aVTTEyMRuadmy5dughdXV2l8b6anHtoaKho0qSJWLVqldi2bZvo06ePkMlkYsmSJUIIzcx9+fLlAoDo1auXuHLlioiOjhadOnUSurq6AoAIDQ0VQmhe7h/js0uDBg1E+fLlVfplZWUJAGL48OFK7XFxcSIyMlKar+TChQtCLpeLq1eviqSkJNGtWzdhb28v6tevL/79998C58Kim4i+OgW50p2b169fi1KlSklFed26dUWHDh3E2bNnRfv27UX9+vXVGPV/8zG+Lc7N33//LYyMjMS9e/fEtWvXhK6urvjll1/EP//8I2xsbMQvv/zysVMplAcPHghXV1fh6Oio9KWIpuf9tm+//VbUqFFDZGdna3TegwYNEqVKlVKaRO+/XOnOzZo1a0SJEiXEixcvxKFDh4SRkZHYsWOH2LlzpzA0NJSuinwOOnfuLPT09ERmZqZG5u3u7i4ASBNBKRw7dkwAEBs2bNDIvN+VmpoqDA0NRfPmzZXaNTX3zZs3CwMDAxEfH6/U3rt3b2FoaCiePHmisbn/8MMPUpENQFSvXl1MnDhRABA7duwQQmjecf/UV7pzU69ePakw79atm/D29hZnzpwRgYGBws3NTWRkZBQoF47pJqKvjp2dHR48eKDSrmizt7fP9XULFiyAjo4OAgMDER8fjxMnTmDOnDnw9PTEnDlzcOzYMdy7d0+tsX9sdnZ2AJDn/rC0tIRcLs/1tVlZWRg+fDjGjx+PEiVKSGMKAwIC4Ovri4EDB2LTpk1qjf99kpOT4e/vj6SkJOzfv1/puGpy3u9q3749zpw5g5iYGI3NOzY2FqtWrcKwYcNw//593LlzB3fu3EF6ejoyMjJw584dPHv2LN/883rvAzkTFE2cOBGzZs2CkZERNm/ejPbt26N169Zo1aoV2rdv/1kdd0dHR7x58wYvX77UyLwVMb87oVbx4sUBAM+fP9fIvN+1c+dOvHr1Ct26dVNq19Tcly1bhqpVq8LBwUGpvWXLlnj16hUuXLigsbmHhITg0aNHCAsLw6VLl3DmzBlp8sAyZcoA0Nzj/q7C/C2zs7PDw4cPIYRQ6Qfk/ZkPALZs2YKrV68iODgYWVlZ2Lp1K6ZMmYLq1atjzpw5uHfvHiIiIgoUM4tuIvrqVKlSBTExMdIsqAqRkZHS8nc9ePAA06dPlwrv+/fvA/i/f6wV/01ISFBj5B9fiRIlYG1trTTpksLp06dz3RcKy5cvR2pqKsaMGQMAuH//vtIfL3t7+yLbH+np6WjRogViYmLw559/okKFCkrLNTXv3ChmXk1OTtbYvBMSEpCdnY1hw4bBxcVF+omMjERMTAxcXFwwdepUVKxYETo6Oir5v3nzBlFRUe/Nf+rUqXBxcZGKm88p/9zcunUL+vr6MDY21si8PT09Aaj+m6v4t9na2loj837Xpk2bYGxsjJYtWyq1a2rujx49QlZWlkp7RkYGACAzM1NjcwdyJkCrW7euNFHk4cOH4eDggHLlygHQ3OP+rsL8LatSpQpevXql9PQS4P2f+QDg1atXGDt2LKZNmwZzc3M8efIEGRkZ0j4xMDCAhYVFgfcJi24i+uq0b98eWVlZWLVqldT2+vVrrFu3DrVq1YKjo6PKa8aPHw9vb2/4+fkB+L+rK9euXQMA6R/zgjzm4nPTrl07/Pnnn4iPj5fa/v77b8TExKBDhw65vubZs2cICgrC3Llzoa+vDyBnnyj2B5CzT4pif2RlZaFTp044deoUfv/9d9SuXTvXfpqW9+PHj1XaMjIysHHjRhgYGEhfPGha3kDOB80dO3ao/Li7u6NkyZLYsWMH+vbtCzMzMzRq1Ai//vorUlNTpdeHhobixYsXeeYfExODpUuXYtGiRdLjZD6X/BMTE1XaLl68iN27d6Nx48bQ0tLSyLw7duwIAFi7dq1S+5o1a6CjowMfHx+NzPttiYmJOHz4MNq0aQNDQ0OlZZqae5kyZXDhwgXExMQotW/evBlaWlrw8PDQ2NzftWXLFpw5cwYjRoyQHuv1teQOFPxvWatWraCrq4tly5ZJbUIIrFixAiVKlICXl1eu6589ezYsLCzQv39/AICVlRV0dHSkffLkyRMkJiYWfJ8U6CZ0IiIN06FDB6GjoyPGjh0rVq5cKby8vISOjo7K+EAhhIiMjBRyuVxcv35dqb169eqiatWqYunSpaJKlSqiVq1anyr8AluyZImYNm2aGDx4sAAg2rZtK6ZNmyamTZsmkpKShBBC3L17V1hZWQk3NzexePFiMWPGDGFhYSEqVaqU5/jeIUOGqIxhv3TpkpDJZGLQoEFi5syZQl9fX2k23U9l+PDhAoBo0aKFCA0NVflR0LS8W7duLRo0aCCCg4PF6tWrxbRp00S5cuUEADF//nypn6bl/T7vjukWQohz584JuVwuqlatKpYvXy4mTpwo9PX1RePGjfNcT9OmTUXPnj2V2vbs2SO0tLTEhAkTxIQJE4SWlpb466+/1JLH+/j6+oqmTZuK6dOni1WrVokRI0YIQ0NDYWZmpjTJj6blLYQQffr0EQBEx44dxc8//yw6dOggAIgJEyZIfTQxb4UlS5YIAGL//v25LtfE3I8dOya0tbVF8eLFxdSpU8XPP/8s/P39BQDRr18/qZ+m5X7s2DHRsGFDMXv2bLFmzRrRr18/oa2tLfz8/FTGFGtC7h/7s4tinPeAAQPE6tWrRbNmzQQAsWnTply3HxcXJwwMDFTGsLdr1064uLiIJUuWiAYNGghHR0eleUTeh0U3EX2V0tLSxJgxY4Stra2Qy+WiRo0auX5wyc7OFrVq1RKjRo1SWXbjxg3h7e0tjI2Nhbe3t7h58+anCL1QnJyclB6r8vbP7du3pX6XL18WjRs3FoaGhsLc3Fx069ZNmvH6XZcuXRJ6enriwoULKsvWr18vnJ2dhZWVlRg1apTS400+lfr16+eZ87vfNWtS3ps3bxaNGjUSNjY2QkdHR1hYWIhGjRqJXbt2qfTVpLzfJ7eiWwghwsLChJeXl9DX1xfW1tbiu+++EykpKbmuY+/evcLY2Fjcv39fZdnMmTOFvb29sLOzE7Nnz/7o8RfEokWLRM2aNYWlpaXQ0dERdnZ2onv37kozzitoUt5CCPHmzRsRHBwsnJychK6urtJEl2/TtLwVvvnmG1G8ePH3vu80MffIyEjh7+8vbG1tha6urihTpowICQlRKT41KfcbN26Ixo0bi2LFigm5XC7KlSsnZs6cmWfB96Xn/rE/u2RlZYkZM2YIJycnoaenJ9zd3cWvv/6a5/Y7dOgg2rZtq9L+6NEj0aJFC2FiYiKqVasmzp49W+CcZEK8M6qciIiIiIiIiD4KjukmIiIiIiIiUhMW3URERERERERqwqKbiIiIiIiISE1YdBMRERERERGpCYtuIiIiIiIiIjVh0U1ERERERESkJiy6iYiIiIiIiNSERTcRERERERGRmrDoJiIiIiIiIlITFt1EREREX6mHDx+iV69ecHR0hLa2NmQyGZKSkoo6rC9e7969IZPJcOfOnaIOhYg+Ayy6iYiIvlJ37tyBTCZT+tHT04OjoyO6du2KS5cuFXWIn8TXXCD17t0boaGh8Pb2xo8//oigoCDo6+sXdVj0ARTv5969exd1KET0Dp2iDoCIiIiKlpubG7p37w4AePHiBSIiIrB582Zs374df//9N+rUqVPEEZI6vHnzBocOHUKjRo2wadOmog6HiEhjsegmIiL6ypUqVQrBwcFKbT/++CNCQkIwceJEHD16tEjiIvV6+PAhsrOzYW9vX9ShEBFpNN5eTkRERCqGDh0KADhz5oxS+65du9CwYUNYWFhAX18fFStWxLx585CVlaXUb/369ZDJZFi/fj327NmDOnXqwMTEBM7OzlKfN2/eYMGCBahRowZMTExgbGyMChUqYNSoUXj+/LnS+h4/foyRI0eiVKlSkMvlKFasGNq1a4fLly+rxO7s7AxnZ2e8ePECw4cPh729PeRyOTw8PPDHH3+o9N2wYQMAwMXFRbrN3sfHR+qzY8cOdOnSBaVKlYKhoSHMzMxQr149bNu2Lc/9t3LlSri7u0NfXx+Ojo4YN24c0tPTVdatkJqaiqCgILi7u8PAwADm5uZo0qQJTpw4kec2cvPy5UsEBQWhXLly0NfXh6WlJZo1a4bw8HClfj4+PnBycgIAbNiwQco7v1uTjx49CplMhuDgYJw8eRKNGzeGubk5ZDKZ1EcIgV9++QV16tSBqakpDA0NUb16dfzyyy8q60tPT8f8+fNRuXJlmJmZwcjICM7OzujYsSMuXrwo9Xv7fNq1axdq1qwJQ0NDWFtbo0+fPnj06FGu8YaHh6NZs2awtLSEvr4+ypUrh6CgILx69Uqlr+LYPHr0CL169UKxYsVgYGCAb775Js8vnq5cuYLmzZvDxMQEZmZmaNq0aa7n5Ns+5D108OBBeHl5wdDQEFZWVujVqxeePn2q1NfFxQWA8vGUyWRS7AXd10T08fFKNxEREeXp7WJqwoQJmDVrFkqUKIG2bdvCzMwMYWFhGDt2LCIjI/H777+rvP7333/HwYMH0bx5cwwZMgQpKSkAgLS0NHz77bcIDw9H6dKlERAQALlcjtjYWKxcuRI9e/aEhYUFAODmzZvw8fHBvXv30LhxY7Ru3RqPHz/Gtm3bcODAAfz999+oVauW0nYzMjLQuHFjPH/+HO3atcOrV6/wv//9Dx07dsT+/fvRuHFjAMCIESOwfv16XLx4EcOHD4e5uTkAKH05MGHCBOjp6aFu3bqws7NDYmIidu/ejfbt22Px4sXSFxQKkydPxrRp02BjY4P+/ftDV1cXW7duxbVr13Ldx8+ePYO3tzeuXLmCOnXqYNCgQUhJScGuXbvg6+uL33//Ha1bt873WKWnp6NBgwY4ffo0qlWrhhEjRuDRo0fYsmULDhw4gM2bN6NDhw4AcsZyV6lSBYsWLULlypWl9VepUiXf7QDAyZMnMWPGDPj6+mLAgAG4e/cugJyCu1u3bti8eTNKly6Nrl27Qk9PD4cOHULfvn3x77//Yt68edJ6evXqha1bt8LDw0M6B+Lj43HkyBGcOXMGlStXVtqu4pi3b98ejRo1QkREBNatW4ewsDCcPn1aOmeAnHOvS5cukMvl6NSpE4oXL46DBw9i6tSpOHDgAI4ePaoyfj0pKQl169aFmZkZevTogcePH2PLli1o0qQJzp07h4oVK0p9L1++jDp16uDFixdo27YtSpcujdOnT6NOnToqcSt8yHto9+7d2Lt3L1q0aAEvLy8cP34cGzduxM2bN6UvZapUqYLhw4erHE/g/87lwu5rIvqIBBEREX2Vbt++LQCIJk2aqCybPHmyACB8fX2FEEIcPHhQ6vvixQupX3Z2thg0aJAAIP744w+pfd26dQKA0NLSEocOHVJZ/+jRowUA0aNHD5GZmam0LCkpSaSmpkq/e3l5CW1tbbF//36lftevXxcmJiaiUqVKSu1OTk4CgGjVqpV4/fq11H748OFc8+3Vq5cAIG7fvp3rfrp586ZKW2pqqqhUqZIwMzMTL1++VIpJW1tblChRQjx69EhqT0lJERUqVBAARP369ZXW1bVrVwFArF69Wqn90aNHwtHRUVhbW4u0tLRcY3vblClTBADRrVs3kZ2dLbWfP39e6OnpCXNzc5GSkiK1K45/r1698l23wpEjRwQAAUD88ssvKstXrVolAIiAgADx5s0bqf3169eiRYsWAoA4e/asECLnOMtkMuHp6alyDmRmZornz59LvyvOJwAq58H48eMFABEYGCi1JScnCzMzMyGXy8XFixel9qysLNGpUycBQEydOlVpPYr1DxkyRGRlZUnta9asEQDEwIEDlfrXr19fABC//vqrUvuECROkdb19Tn3oe0hHR0ecOHFCad/4+PgIAOLUqVNS+/uOZ2H2NRF9fCy6iYiIvlKKD+lubm4iKChIBAUFiTFjxoh69eoJAEJfX1+cPHlSCCFEy5YtBQARFxensh7FB/p27dpJbYqCoU2bNir9MzIyhImJiTAzMxPPnj17b4znz58XAESfPn1yXT5q1CgBQERHR0ttiqL71q1bKv2dnJyEpaWlUlt+RXde5s+fLwCIo0ePSm3BwcECgPjpp59U+v/2228qRXdiYqLQ1tYWDRo0yHUbixcvFgDEnj178o3H1dVV6Orqivj4eJVl/fv3FwDExo0bpbb/UnRXq1Yt1+UeHh7CyMhIvHr1SmXZpUuXBAAxevRoIUROYQxA1KlTR+lLgtwozqdGjRqpLEtNTRXm5ubC1NRUKpY3btwoAIjBgwer9I+LixM6OjrC1dVVqR2AMDIyUvrCR4ic81VHR0cp57i4OAFAeHh45BnPu+fUh76Hevbsmef+WLx4sdT2vuNZmH1NRB8fby8nIiL6yt28eRNTpkwBAOjq6sLGxgZdu3bF+PHjUalSJQBAREQEjIyMch2XCwAGBga53j5ds2ZNlbZr164hNTUVjRo1UrodODcREREAgEePHqlM9qZYl+K/b9/6a25uLo1xfZuDgwNOnTr13m2+6/Hjx5g1axb27duHuLg4pKWlKS2/f/++9P+KsbF169ZVWU9us8CfOXMGWVlZeP36da75xcbGAsjJr3nz5nnGmJKSglu3bqF8+fJwcHBQWe7r64vVq1cjKioKPXr0yHM9BVWjRg2VtlevXiE6Ohr29vaYPXu2yvKMjAwA/3fMTE1N0bRpU/z111+oVq0aOnToAB8fH9SoUQO6urq5brdevXoqbcbGxqhSpQqOHj2KW7duoVSpUrhw4QIA5Dp+vmTJknB1dUVMTAxSU1NhYmIiLStTpgyMjY2V+uvo6MDGxkbp+eXvO85vx/O2D30PeXp6qrQpjnFBn6n+IfuaiD4eFt1ERERfuSZNmmD//v3v7fPs2TNkZmZKxXluXr58qdJmY2Oj0pacnAwAKFGiRL6xPXv2DACwd+9e7N27t8DbNjMzy7Wfjo4OsrOz893u29uvUaMG7t69izp16qBRo0YwNzeHtrY2oqKisGvXLrx+/VrqrxizXrx4cZV15bYvFPmFh4erTHb2ttz27dsU281tGwBgZ2en1O+/ym07z58/hxACCQkJBT5Pfv/9d8yYMQO//fYbJk6cCCCnQAwICMCMGTNgaGiY73bfblecWwXZHzExMUhJSVEquk1NTXPtr6OjozTRmWI7uR3nvLb7oe+h3GLS0cn5CP/u5GvvU9h9TUQfD2cvJyIionyZmprCysoKImdoWq4/t2/fVnnd2xOxKSgmK0tISCjQdgFgyZIl7912r169/luCeVi7di3u3r2LadOm4cSJE1iyZAmmTZuG4OBgfPPNN3nG+/jxY5Vluc2wreg/evTo9+YXFBT03jgV68lrFu+HDx8q9fuvcjuuinV7enq+N5cjR45IrzE0NMT06dNx69Yt3Lp1C2vXrkXZsmWxaNEijBw5UmUbeeWnaFd82aLu/aHYTm7HOa/tfuh76GMp7L4moo+HRTcRERHlq1atWnj69Kl0u/N/UbZsWZiamuLMmTMqjwbLbbsACn1LeGFoa2sDyP2q4c2bNwEArVq1UlkWFham0qaYATq3q9YnT55UaatRowZkMtl/zs/U1BSurq64ceNGrl9mKG51Lujs5B/CxMQE5cuXx9WrVwt82/PbXFxc0KdPHxw7dgzGxsbYvXu3Sp/c9vmLFy8QFRUl7QMAqFq1KgDk+qiv+Ph43Lx5E66urkpXuQtDcZxze6SbIp53fcz3UG7edx6/qyD7mog+HhbdRERElK9hw4YBAPr06aP0fGCFhw8f4urVqwVal46ODgYOHIjk5GQMHz5cpUhITk7GixcvAOSMCa9VqxY2b96MLVu2qKwrOzsbx44dK2w6SiwtLQHkFGPvUjzL+t3i6rfffsNff/2l0r9z587Q0tLC/Pnz8eTJE6n95cuXCAkJUelva2uLjh074uTJk5g7dy6EECp9IiMjc32u9Lt69eqFjIwMTJgwQWk9ly5dwvr162FmZlagR4/9F8OGDcOrV6/Qv3//XG+Vvn37Nu7cuQMASExMzPWZ1s+fP8fr169VHucFAIcPH8aBAweU2kJCQpCUlISePXtCSyvno22rVq1gZmaGdevW4cqVK1JfIQS+//57ZGZm5vtM8vcpWbIkvL29cenSJWzatElp2YwZM3L90uFjvodyY2FhAZlMlut5/CH7mog+Ho7pJiIionz5+flh0qRJmDZtGkqVKgU/Pz84OTnh6dOnuHHjBsLCwjB9+nSUL1++QOubOnUqIiIiEBoaioiICPj7+0Mul+PWrVvYv38/Tpw4IV2V3bx5M3x9fdG5c2csXLgQ1apVg4GBAe7evYtTp04hMTER6enpH5xbgwYNMG/ePAwYMADt2rWDkZERnJyc0KNHD/To0QOzZ8/G0KFDceTIETg5OeHixYv4+++/0bZtW2zfvl1pXWXLlsX48eMxY8YMVKpUCR07doSOjg62b9+OSpUq4fLly1JhqLBs2TJcv34d48aNQ2hoKGrXrg1zc3PEx8fj7NmziI2NxYMHD/Idcztu3Djs3bsXoaGhuHr1Kho2bCg9ZzozMxOrV6/+4Cu7BTVw4EBERERgw4YNCA8PR6NGjWBvb49Hjx7h2rVriIyMxG+//QZnZ2ckJCSgatWqqFy5Mjw8PFCiRAk8ffoUu3btQkZGBsaMGaOy/ubNm6NFixZo3749nJ2dERERgSNHjsDNzQ1Tp06V+pmammL16tXo0qULatWqhU6dOsHa2hqHDx/GuXPnULNmTYwdO/Y/5frzzz+jTp066NmzJ3bu3Ck9p/vMmTOoV6+eylX5j/0eepexsTFq1KiB48ePo0ePHihdujS0tLTQo0cPPH/+vND7mog+IjXOjE5ERESfsfc9pzsvhw4dEi1atBDW1tZCV1dX2Nraitq1a4tp06aJu3fvSv0UjzRat25dnutKT08X8+bNE1WqVBEGBgbC2NhYVKhQQYwePVrlucHPnj0TP/74o6hYsaLUt3Tp0qJr165i+/btSn2dnJyEk5NTrttUPFv5XXPmzBGlS5cWurq6Ko/1ioqKEo0bNxYWFhbCxMRE1K9fXxw+fPi9OS5btkyUL19e6OnpCQcHBzFmzBgRHx8vPT/8Xa9evRJz5swRnp6ewsjISBgYGAgXFxfRunVrsXHjRpGRkZHnfnzbixcvxKRJk0SZMmWkZ3P7+/uLsLAwlb7/5ZFhQUFB7+23ZcsW0ahRI2FhYSF0dXVFiRIlhI+Pj5g/f75ITEwUQgjx/PlzERwcLLy9vYWdnZ3Q09MT9vb2ws/PT+zbt09pfW/v6507d4oaNWoIAwMDYWVlJXr37i0ePHiQaxzHjx8X/v7+wtzcXOjp6YkyZcqISZMmKT0nW+Hd4/62vM6p6Oho0bRpU2FsbCxMTEyEv7+/iI6Ofu9j6D7Geyiv43D9+nXRtGlTYW5uLmQymQAgjhw5Uqh9TUQfn0yIXO5jIiIiIqKP6vDhw/j2228xbty4XB+pRXlbv349AgICsG7duv90WzgRUVHgmG4iIiKijygxMVFlnHpSUhImTJgAAGofV01ERJ8XjukmIiIi+og2bdqEefPmoUGDBrC3t8eDBw+wf/9+PH78GL1790bt2rWLOkQiIvqEWHQTERERfUReXl7w9PTE4cOH8ezZM2hra6N8+fKYNGkShgwZUtThERHRJ8Yx3URERERERERqwjHdRERERERERGrCopuIiIiIiIhITVh0ExEREREREakJi24iIiIiIiIiNWHRTURERERERKQmLLqJiIiIiIiI1IRFNxEREREREZGasOgmIiIiIiIiUhMW3URERERERERq8v8Ato4Hecqe2qYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define new categories based on the conditions\n",
        "df_temp = df_wiring_inspection['Spotlight Kampala Wiring Ins...']\n",
        "\n",
        "df_temp['Wiring Condition'] = 'Other'\n",
        "df_temp.loc[df_temp['6.1 Are there visible conductors/wires inside or coming into this building?'] == 'No', 'Wiring Condition'] = 'No visible wiring'\n",
        "df_temp.loc[(df_temp['6.1 Are there visible conductors/wires inside or coming into this building?'] == 'Yes') &\n",
        "            (df_temp['6.4 Are any of the visible conductors exposed (without a jacket or connectors) or improperly spliced?'] == 'Yes'), 'Wiring Condition'] = 'Wiring exposed or improperly spliced'\n",
        "df_temp.loc[(df_temp['6.1 Are there visible conductors/wires inside or coming into this building?'] == 'Yes') &\n",
        "            (df_temp['6.4 Are any of the visible conductors exposed (without a jacket or connectors) or improperly spliced?'] == 'No'), 'Wiring Condition'] = 'Wiring in good condition'\n",
        "\n",
        "# Remove \"No visible wiring\" category and reweight the percentages\n",
        "filtered_df_temp = df_temp[df_temp['Wiring Condition'] != 'No visible wiring']\n",
        "\n",
        "# Count occurrences for each category\n",
        "wiring_counts = filtered_df_temp['Wiring Condition'].value_counts()\n",
        "\n",
        "# Reweight percentages based on the remaining categories\n",
        "wiring_percentages = (wiring_counts / wiring_counts.sum()) * 100\n",
        "wiring_percentages = wiring_percentages.sort_values(ascending=True)  # Sort for horizontal bar chart\n",
        "\n",
        "# Plot the horizontal bar chart\n",
        "plt.figure(figsize=(10, 2))\n",
        "bars = plt.barh(wiring_percentages.index, wiring_percentages, color='#71b3ff', edgecolor='black', alpha=0.8)\n",
        "\n",
        "# Add data labels\n",
        "for bar in bars:\n",
        "    plt.text(bar.get_width() + 1, bar.get_y() + bar.get_height() / 2,\n",
        "             f\"{bar.get_width():.0f}%\", va='center', ha='left', fontsize=12, color='black')\n",
        "\n",
        "# Add labels and title\n",
        "#plt.title('Condition of visible wiring in buildings', fontsize=16)\n",
        "plt.xlabel('Percentage of respondents', fontsize=14)\n",
        "plt.xticks(\n",
        "    ticks=range(0, 101, 10),  # Tick intervals at 10%\n",
        "    labels=[f\"{tick}%\" for tick in range(0, 101, 10)],\n",
        "    fontsize=12\n",
        ")\n",
        "plt.yticks(fontsize=12)  # Ensure consistent font size for y-tick labels\n",
        "\n",
        "# Bound x-axis at 100%\n",
        "plt.xlim(0, 100)\n",
        "\n",
        "# Remove the y-axis label and gridlines\n",
        "plt.ylabel(None)  # Remove the y-axis label\n",
        "plt.grid(False)  # Remove gridlines\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(fig_path + \"Wiring Condition Results.png\", dpi=500)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAIIf1psYlZK"
      },
      "source": [
        "# Electricity consumption data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HYeO685l9MhV"
      },
      "outputs": [],
      "source": [
        "# Set lower and upper voltage bounds\n",
        "nominal_voltage = 240\n",
        "\n",
        "# Accepted bounds = +/- 6% per Electricity Regulatory\n",
        "lower_bound = 0.94 * nominal_voltage\n",
        "upper_bound = 1.06 * nominal_voltage"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Threshold Settings ---\n",
        "MIN_DURATION_MIN = 5           # in minutes\n",
        "MAX_DURATION_MIN = 90          # in minutes\n",
        "MIN_START_WATT = 300           # in watts"
      ],
      "metadata": {
        "id": "NN0mKfmOpK0Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Kosko sensor analysis"
      ],
      "metadata": {
        "id": "GwFts5ferXwj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a copy of the original DataFrame to avoid modifying it in place\n",
        "df_consumption_monitoring_kosko_edit = df_consumption_monitoring_kosko.copy()"
      ],
      "metadata": {
        "id": "HiUBHoCerLlc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Count and drop NaN values in \"DEVICE ID\" column\n",
        "nan_count = df_consumption_monitoring_kosko_edit[\"DEVICE ID\"].isna().sum()\n",
        "print(f\"🔍 Number of rows with NaN in 'DEVICE ID': {nan_count}\")\n",
        "\n",
        "df_consumption_monitoring_kosko_edit = df_consumption_monitoring_kosko_edit.dropna(subset=[\"DEVICE ID\"])\n",
        "display(df_consumption_monitoring_kosko_edit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "qtqZOdftrPpK",
        "outputId": "640d8ddc-9224-4a0f-ed33-4350176522f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🔍 Number of rows with NaN in 'DEVICE ID': 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    TIME  VOLTAGE CURRENT   WATT     KWH DEVICE STATUS  \\\n",
              "0      23-06-11 00:10:07  216.332   0.028  3.484  14.142           OFF   \n",
              "1      23-06-11 00:25:07  208.132   0.028  3.282  14.143           OFF   \n",
              "2      23-06-11 00:40:07  220.521   0.028  3.105  14.144           OFF   \n",
              "3      23-06-11 00:55:07   211.65   0.027  3.214  14.145           OFF   \n",
              "4      23-06-11 01:10:07  205.057   0.027  3.115  14.145           OFF   \n",
              "...                  ...      ...     ...    ...     ...           ...   \n",
              "67978  23-06-10 18:01:56  234.596   0.025  0.124  42.574           OFF   \n",
              "67979  23-06-10 18:16:56  249.044   0.025  0.091  42.574           OFF   \n",
              "67980  23-06-10 18:31:56  241.473   0.025  0.138  42.574           OFF   \n",
              "67981  23-06-10 18:46:56  233.331   0.025  0.083  42.574           OFF   \n",
              "67982  23-06-10 23:36:33   209.86   0.025  0.047  42.574           OFF   \n",
              "\n",
              "                    DEVICE ID  \n",
              "0           EM_073_2023-06-27  \n",
              "1           EM_073_2023-06-27  \n",
              "2           EM_073_2023-06-27  \n",
              "3           EM_073_2023-06-27  \n",
              "4           EM_073_2023-06-27  \n",
              "...                       ...  \n",
              "67978  061023-2131-EM105_v2.1  \n",
              "67979  061023-2131-EM105_v2.1  \n",
              "67980  061023-2131-EM105_v2.1  \n",
              "67981  061023-2131-EM105_v2.1  \n",
              "67982  061023-2131-EM105_v2.1  \n",
              "\n",
              "[67983 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1afc8b76-02da-49ab-8c0f-4b8923020ec4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TIME</th>\n",
              "      <th>VOLTAGE</th>\n",
              "      <th>CURRENT</th>\n",
              "      <th>WATT</th>\n",
              "      <th>KWH</th>\n",
              "      <th>DEVICE STATUS</th>\n",
              "      <th>DEVICE ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23-06-11 00:10:07</td>\n",
              "      <td>216.332</td>\n",
              "      <td>0.028</td>\n",
              "      <td>3.484</td>\n",
              "      <td>14.142</td>\n",
              "      <td>OFF</td>\n",
              "      <td>EM_073_2023-06-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23-06-11 00:25:07</td>\n",
              "      <td>208.132</td>\n",
              "      <td>0.028</td>\n",
              "      <td>3.282</td>\n",
              "      <td>14.143</td>\n",
              "      <td>OFF</td>\n",
              "      <td>EM_073_2023-06-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23-06-11 00:40:07</td>\n",
              "      <td>220.521</td>\n",
              "      <td>0.028</td>\n",
              "      <td>3.105</td>\n",
              "      <td>14.144</td>\n",
              "      <td>OFF</td>\n",
              "      <td>EM_073_2023-06-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23-06-11 00:55:07</td>\n",
              "      <td>211.65</td>\n",
              "      <td>0.027</td>\n",
              "      <td>3.214</td>\n",
              "      <td>14.145</td>\n",
              "      <td>OFF</td>\n",
              "      <td>EM_073_2023-06-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>23-06-11 01:10:07</td>\n",
              "      <td>205.057</td>\n",
              "      <td>0.027</td>\n",
              "      <td>3.115</td>\n",
              "      <td>14.145</td>\n",
              "      <td>OFF</td>\n",
              "      <td>EM_073_2023-06-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67978</th>\n",
              "      <td>23-06-10 18:01:56</td>\n",
              "      <td>234.596</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.124</td>\n",
              "      <td>42.574</td>\n",
              "      <td>OFF</td>\n",
              "      <td>061023-2131-EM105_v2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67979</th>\n",
              "      <td>23-06-10 18:16:56</td>\n",
              "      <td>249.044</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.091</td>\n",
              "      <td>42.574</td>\n",
              "      <td>OFF</td>\n",
              "      <td>061023-2131-EM105_v2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67980</th>\n",
              "      <td>23-06-10 18:31:56</td>\n",
              "      <td>241.473</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.138</td>\n",
              "      <td>42.574</td>\n",
              "      <td>OFF</td>\n",
              "      <td>061023-2131-EM105_v2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67981</th>\n",
              "      <td>23-06-10 18:46:56</td>\n",
              "      <td>233.331</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.083</td>\n",
              "      <td>42.574</td>\n",
              "      <td>OFF</td>\n",
              "      <td>061023-2131-EM105_v2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67982</th>\n",
              "      <td>23-06-10 23:36:33</td>\n",
              "      <td>209.86</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.047</td>\n",
              "      <td>42.574</td>\n",
              "      <td>OFF</td>\n",
              "      <td>061023-2131-EM105_v2.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>67983 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1afc8b76-02da-49ab-8c0f-4b8923020ec4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-1afc8b76-02da-49ab-8c0f-4b8923020ec4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-2e56e606-9810-4231-b96c-17dbb0f99d1c button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_4c886aeb-4544-4ccd-b5ae-9e4893345fe5\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_consumption_monitoring_kosko_edit",
              "summary": "{\n  \"name\": \"df_consumption_monitoring_kosko_edit\",\n  \"rows\": 67983,\n  \"fields\": [\n    {\n      \"column\": \"TIME\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 67425,\n        \"samples\": [\n          \"23-04-26 17:18:41\",\n          \"23-06-22 11:36:58\",\n          \"23-05-26 01:08:56\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VOLTAGE\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 45374,\n        \"samples\": [\n          235.286,\n          199.841,\n          215.238\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CURRENT\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4528,\n        \"samples\": [\n          0.064,\n          3.939,\n          7.034\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WATT\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 26900,\n        \"samples\": [\n          \"0.016\",\n          37.457,\n          877.992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KWH\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 30348,\n        \"samples\": [\n          41.556,\n          21.72,\n          23.848\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DEVICE STATUS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"OFF\",\n          \"ON\",\n          \"DEVICE STATUS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DEVICE ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"EM_089_2023-06-27\",\n          \"EM_095_2023-06-27\",\n          \"EM_070_2023-06-17\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the \"TIME\" column (string) to datetime and apply timezone correction\n",
        "df_consumption_monitoring_kosko_edit[\"DateTime\"] = pd.to_datetime(\n",
        "    df_consumption_monitoring_kosko_edit[\"TIME\"], format=\"%y-%m-%d %H:%M:%S\", errors=\"coerce\"\n",
        ") - pd.Timedelta(hours=4.5)\n",
        "\n",
        "# Sort by DEVICE ID and DateTime\n",
        "df_consumption_monitoring_kosko_edit = df_consumption_monitoring_kosko_edit.sort_values(\n",
        "    by=[\"DEVICE ID\", \"DateTime\"]\n",
        ").reset_index(drop=True)"
      ],
      "metadata": {
        "id": "H26td97OrdVP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cooking_events = []\n",
        "\n",
        "for device_id, group in df_consumption_monitoring_kosko_edit.groupby(\"DEVICE ID\"):\n",
        "    group = group.sort_values(by=\"DateTime\").reset_index(drop=True)\n",
        "\n",
        "    on_events = group[\"DEVICE STATUS\"].str.strip().eq(\"ON\")\n",
        "    off_events = group[\"DEVICE STATUS\"].str.strip().eq(\"OFF\")\n",
        "\n",
        "    start_times = group.loc[on_events, \"DateTime\"].values\n",
        "    end_times = group.loc[off_events, \"DateTime\"].values\n",
        "\n",
        "    start_voltages = pd.to_numeric(group.loc[on_events, \"VOLTAGE\"], errors='coerce').values\n",
        "    end_voltages = pd.to_numeric(group.loc[off_events, \"VOLTAGE\"], errors='coerce').values\n",
        "\n",
        "    start_watts = pd.to_numeric(group.loc[on_events, \"WATT\"], errors='coerce').values\n",
        "    end_watts = pd.to_numeric(group.loc[off_events, \"WATT\"], errors='coerce').values\n",
        "\n",
        "    min_length = min(len(start_times), len(end_times))\n",
        "\n",
        "    if min_length > 0:\n",
        "        durations = (end_times[:min_length] - start_times[:min_length]) / np.timedelta64(1, 'm')\n",
        "        voltage_diffs = end_voltages[:min_length] - start_voltages[:min_length]\n",
        "        watt_diffs = end_watts[:min_length] - start_watts[:min_length]\n",
        "\n",
        "        cooking_events.extend(zip(\n",
        "            [device_id] * min_length,\n",
        "            start_times[:min_length],\n",
        "            end_times[:min_length],\n",
        "            durations,\n",
        "            start_voltages[:min_length],\n",
        "            end_voltages[:min_length],\n",
        "            voltage_diffs,\n",
        "            start_watts[:min_length],\n",
        "            end_watts[:min_length],\n",
        "            watt_diffs\n",
        "        ))\n",
        "\n",
        "cooking_df = pd.DataFrame(cooking_events, columns=[\n",
        "    \"DEVICE ID\", \"START TIME\", \"END TIME\", \"DURATION\",\n",
        "    \"START VOLTAGE\", \"END VOLTAGE\", \"VOLTAGE DIFFERENCE\",\n",
        "    \"START WATT\", \"END WATT\", \"WATT DIFFERENCE\"\n",
        "])\n",
        "\n",
        "cooking_df = cooking_df[cooking_df[\"DURATION\"] >= 0]"
      ],
      "metadata": {
        "id": "3MduM8IQsck4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter by duration\n",
        "filtered_df = cooking_df[(cooking_df[\"DURATION\"] > MIN_DURATION_MIN) & (cooking_df[\"DURATION\"] <= MAX_DURATION_MIN)]\n",
        "\n",
        "# Further filter by end wattage\n",
        "filtered_df = filtered_df[filtered_df[\"START WATT\"] >= MIN_START_WATT]\n",
        "\n",
        "display(filtered_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Ku7W2tjssqFI",
        "outputId": "e11edd7f-4af9-470c-c77a-d4e2bf165148"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "               DEVICE ID          START TIME            END TIME   DURATION  \\\n",
              "1974   EM_064_2023-06-17 2022-09-30 06:09:35 2022-09-30 06:14:48   5.216667   \n",
              "2339   EM_066_2023-06-17 2022-09-30 06:20:39 2022-09-30 06:27:54   7.250000   \n",
              "3346   EM_074_2023-07-01 2022-10-04 07:05:57 2022-10-04 07:11:24   5.450000   \n",
              "3364   EM_075_2023-06-17 2022-10-04 06:31:31 2022-10-04 06:36:45   5.233333   \n",
              "5547   EM_088_2023-06-27 2023-06-09 09:36:56 2023-06-09 09:59:57  23.016667   \n",
              "...                  ...                 ...                 ...        ...   \n",
              "20403  EM_089_2023-06-27 2023-06-25 12:30:34 2023-06-25 13:00:57  30.383333   \n",
              "20404  EM_089_2023-06-27 2023-06-25 12:31:03 2023-06-25 13:01:27  30.400000   \n",
              "20405  EM_089_2023-06-27 2023-06-25 12:31:37 2023-06-25 13:01:58  30.350000   \n",
              "20471  EM_093_2023-06-27 2022-10-04 07:26:04 2022-10-04 07:32:54   6.833333   \n",
              "20544  EM_094_2023-07-01 2022-10-06 11:32:46 2022-10-06 11:38:13   5.450000   \n",
              "\n",
              "       START VOLTAGE  END VOLTAGE  VOLTAGE DIFFERENCE  START WATT  END WATT  \\\n",
              "1974         225.854      229.924               4.070    1203.236     1.170   \n",
              "2339         226.197      229.342               3.145     920.516     1.054   \n",
              "3346         228.993      233.668               4.675    1401.548     1.253   \n",
              "3364         232.574      236.187               3.613     940.402     1.265   \n",
              "5547         221.481      223.968               2.487     878.478     0.118   \n",
              "...              ...          ...                 ...         ...       ...   \n",
              "20403        204.597      214.769              10.172     889.388     0.406   \n",
              "20404        210.098      212.791               2.693     937.005     0.134   \n",
              "20405        202.061      214.033              11.972     868.114     0.412   \n",
              "20471        228.483      239.033              10.550     933.772     1.321   \n",
              "20544        240.320      243.652               3.332     965.629     1.096   \n",
              "\n",
              "       WATT DIFFERENCE  \n",
              "1974         -1202.066  \n",
              "2339          -919.462  \n",
              "3346         -1400.295  \n",
              "3364          -939.137  \n",
              "5547          -878.360  \n",
              "...                ...  \n",
              "20403         -888.982  \n",
              "20404         -936.871  \n",
              "20405         -867.702  \n",
              "20471         -932.451  \n",
              "20544         -964.533  \n",
              "\n",
              "[8869 rows x 10 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DEVICE ID</th>\n",
              "      <th>START TIME</th>\n",
              "      <th>END TIME</th>\n",
              "      <th>DURATION</th>\n",
              "      <th>START VOLTAGE</th>\n",
              "      <th>END VOLTAGE</th>\n",
              "      <th>VOLTAGE DIFFERENCE</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1974</th>\n",
              "      <td>EM_064_2023-06-17</td>\n",
              "      <td>2022-09-30 06:09:35</td>\n",
              "      <td>2022-09-30 06:14:48</td>\n",
              "      <td>5.216667</td>\n",
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              "      <td>229.924</td>\n",
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              "      <th>2339</th>\n",
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              "      <td>2022-09-30 06:27:54</td>\n",
              "      <td>7.250000</td>\n",
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              "      <td>229.342</td>\n",
              "      <td>3.145</td>\n",
              "      <td>920.516</td>\n",
              "      <td>1.054</td>\n",
              "      <td>-919.462</td>\n",
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              "      <th>3346</th>\n",
              "      <td>EM_074_2023-07-01</td>\n",
              "      <td>2022-10-04 07:05:57</td>\n",
              "      <td>2022-10-04 07:11:24</td>\n",
              "      <td>5.450000</td>\n",
              "      <td>228.993</td>\n",
              "      <td>233.668</td>\n",
              "      <td>4.675</td>\n",
              "      <td>1401.548</td>\n",
              "      <td>1.253</td>\n",
              "      <td>-1400.295</td>\n",
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              "    <tr>\n",
              "      <th>3364</th>\n",
              "      <td>EM_075_2023-06-17</td>\n",
              "      <td>2022-10-04 06:31:31</td>\n",
              "      <td>2022-10-04 06:36:45</td>\n",
              "      <td>5.233333</td>\n",
              "      <td>232.574</td>\n",
              "      <td>236.187</td>\n",
              "      <td>3.613</td>\n",
              "      <td>940.402</td>\n",
              "      <td>1.265</td>\n",
              "      <td>-939.137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5547</th>\n",
              "      <td>EM_088_2023-06-27</td>\n",
              "      <td>2023-06-09 09:36:56</td>\n",
              "      <td>2023-06-09 09:59:57</td>\n",
              "      <td>23.016667</td>\n",
              "      <td>221.481</td>\n",
              "      <td>223.968</td>\n",
              "      <td>2.487</td>\n",
              "      <td>878.478</td>\n",
              "      <td>0.118</td>\n",
              "      <td>-878.360</td>\n",
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              "    <tr>\n",
              "      <th>20403</th>\n",
              "      <td>EM_089_2023-06-27</td>\n",
              "      <td>2023-06-25 12:30:34</td>\n",
              "      <td>2023-06-25 13:00:57</td>\n",
              "      <td>30.383333</td>\n",
              "      <td>204.597</td>\n",
              "      <td>214.769</td>\n",
              "      <td>10.172</td>\n",
              "      <td>889.388</td>\n",
              "      <td>0.406</td>\n",
              "      <td>-888.982</td>\n",
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              "    <tr>\n",
              "      <th>20404</th>\n",
              "      <td>EM_089_2023-06-27</td>\n",
              "      <td>2023-06-25 12:31:03</td>\n",
              "      <td>2023-06-25 13:01:27</td>\n",
              "      <td>30.400000</td>\n",
              "      <td>210.098</td>\n",
              "      <td>212.791</td>\n",
              "      <td>2.693</td>\n",
              "      <td>937.005</td>\n",
              "      <td>0.134</td>\n",
              "      <td>-936.871</td>\n",
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              "      <th>20405</th>\n",
              "      <td>EM_089_2023-06-27</td>\n",
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              "      <td>2023-06-25 13:01:58</td>\n",
              "      <td>30.350000</td>\n",
              "      <td>202.061</td>\n",
              "      <td>214.033</td>\n",
              "      <td>11.972</td>\n",
              "      <td>868.114</td>\n",
              "      <td>0.412</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-383de58b-dfb2-4677-a8a4-5f5d0417302b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-6f4b9209-7799-4e6f-a086-20e9db2c7f65\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f4b9209-7799-4e6f-a086-20e9db2c7f65')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-6f4b9209-7799-4e6f-a086-20e9db2c7f65 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_2db735f5-d473-4c6a-bd5c-54a6eac2fc59\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('filtered_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2db735f5-d473-4c6a-bd5c-54a6eac2fc59 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('filtered_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "filtered_df",
              "summary": "{\n  \"name\": \"filtered_df\",\n  \"rows\": 8869,\n  \"fields\": [\n    {\n      \"column\": \"DEVICE ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"EM_066_2023-06-17\",\n          \"EM_089_2023-06-27\",\n          \"EM_064_2023-06-17\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"START TIME\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2022-09-30 06:09:35\",\n        \"max\": \"2023-06-25 12:31:37\",\n        \"num_unique_values\": 8864,\n        \"samples\": [\n          \"2023-06-22 10:38:51\",\n          \"2023-06-15 15:41:16\",\n          \"2023-06-21 19:30:48\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"END TIME\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2022-09-30 06:14:48\",\n        \"max\": \"2023-06-25 13:01:58\",\n        \"num_unique_values\": 8868,\n        \"samples\": [\n          \"2023-06-12 06:27:01\",\n          \"2023-06-16 05:48:03\",\n          \"2023-06-11 06:45:09\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DURATION\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.951937586946169,\n        \"min\": 5.033333333333333,\n        \"max\": 89.48333333333333,\n        \"num_unique_values\": 1730,\n        \"samples\": [\n          13.933333333333334,\n          20.416666666666668,\n          22.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"START VOLTAGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.722489522001668,\n        \"min\": 165.622,\n        \"max\": 244.302,\n        \"num_unique_values\": 7404,\n        \"samples\": [\n          221.779,\n          235.297,\n          222.414\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"END VOLTAGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.620207277696618,\n        \"min\": 0.0,\n        \"max\": 247.245,\n        \"num_unique_values\": 7244,\n        \"samples\": [\n          219.139,\n          245.014,\n          238.317\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VOLTAGE DIFFERENCE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.869136804855994,\n        \"min\": -220.826,\n        \"max\": 52.58100000000002,\n        \"num_unique_values\": 7504,\n        \"samples\": [\n          16.717000000000013,\n          8.091999999999985,\n          5.062999999999988\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"START WATT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 84.83204765667766,\n        \"min\": 300.345,\n        \"max\": 1401.548,\n        \"num_unique_values\": 8612,\n        \"samples\": [\n          851.475,\n          971.751,\n          924.078\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"END WATT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 66.0013561291155,\n        \"min\": 0.0,\n        \"max\": 999.513,\n        \"num_unique_values\": 626,\n        \"samples\": [\n          0.427,\n          0.36,\n          0.176\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WATT DIFFERENCE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 107.75067022148569,\n        \"min\": -1400.295,\n        \"max\": 451.934,\n        \"num_unique_values\": 8739,\n        \"samples\": [\n          -969.321,\n          -855.724,\n          -764.1550000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## A2EI sensor analysis"
      ],
      "metadata": {
        "id": "vUs2iHZntYtd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a copy of the original DataFrame to avoid modifying it in place\n",
        "df_consumption_monitoring_a2ei_edit = df_consumption_monitoring_a2ei.copy()"
      ],
      "metadata": {
        "id": "GIHzqhAZtdQ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_consumption_monitoring_a2ei_edit = df_consumption_monitoring_a2ei_edit.dropna(subset=[\"account_id\"])\n",
        "df_consumption_monitoring_a2ei_edit[\"measurementTime\"] = pd.to_datetime(df_consumption_monitoring_a2ei_edit[\"measurementTime\"])"
      ],
      "metadata": {
        "id": "O6zdKcJOt0Yi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a power threshold to flag cooking\n",
        "df_consumption_monitoring_a2ei_edit[\"is_cooking\"] = df_consumption_monitoring_a2ei_edit[\"meteredPower\"] >= MIN_START_WATT\n",
        "\n",
        "# Identify changes in cooking state\n",
        "df_consumption_monitoring_a2ei_edit[\"cooking_change\"] = df_consumption_monitoring_a2ei_edit[\"is_cooking\"].ne(\n",
        "    df_consumption_monitoring_a2ei_edit[\"is_cooking\"].shift()\n",
        ")\n",
        "\n",
        "# Assign a session ID to each continuous block of cooking/non-cooking\n",
        "df_consumption_monitoring_a2ei_edit[\"cooking_session\"] = df_consumption_monitoring_a2ei_edit[\"cooking_change\"].cumsum()\n",
        "\n",
        "# Extract features for cooking sessions\n",
        "cooking_sessions = df_consumption_monitoring_a2ei_edit[df_consumption_monitoring_a2ei_edit[\"is_cooking\"]].groupby(\n",
        "    [\"account_id\", \"cooking_session\"]\n",
        ").agg({\n",
        "    \"measurementTime\": [\"first\", \"last\"],\n",
        "    \"meteredPower\": \"mean\",\n",
        "    \"meteredVoltageA\": \"mean\"\n",
        "})\n",
        "\n",
        "# Flatten multi-level column names\n",
        "cooking_sessions.columns = [\"start_time\", \"end_time\", \"avg_power\", \"avg_voltage\"]\n",
        "cooking_sessions = cooking_sessions.reset_index()\n",
        "\n",
        "# Calculate session duration in minutes\n",
        "cooking_sessions[\"duration_min\"] = (\n",
        "    cooking_sessions[\"end_time\"] - cooking_sessions[\"start_time\"]\n",
        ").dt.total_seconds() / 60\n",
        "\n",
        "# Filter sessions by duration and power threshold\n",
        "cooking_sessions = cooking_sessions[\n",
        "    (cooking_sessions[\"duration_min\"] >= MIN_DURATION_MIN) &\n",
        "    (cooking_sessions[\"duration_min\"] <= MAX_DURATION_MIN) &\n",
        "    (cooking_sessions[\"avg_power\"] >= MIN_START_WATT)\n",
        "]\n",
        "\n",
        "cooking_sessions.duration_min.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "VvsyXMdmt8Qh",
        "outputId": "20bd11bf-d93b-4480-d22f-f899fa5ff8e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1758.000000\n",
              "mean       10.594606\n",
              "std        10.362144\n",
              "min         5.000000\n",
              "25%         6.000000\n",
              "50%         8.000000\n",
              "75%        10.995833\n",
              "max        90.000000\n",
              "Name: duration_min, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration_min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1758.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10.594606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.362144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>10.995833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 559
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pool cooking events"
      ],
      "metadata": {
        "id": "5ZlS2ZlQumtU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Rename columns in A2EI sessions to match the unified format\n",
        "cooking_sessions_renamed = cooking_sessions.rename(columns={\n",
        "    \"account_id\": \"DEVICE ID\",\n",
        "    \"start_time\": \"START TIME\",\n",
        "    \"end_time\": \"END TIME\",\n",
        "    \"avg_power\": \"START WATT\",  # assume START WATT == avg_power\n",
        "    \"avg_voltage\": \"START VOLTAGE\",\n",
        "    \"duration_min\": \"DURATION\"\n",
        "})\n",
        "\n",
        "# For A2EI, we don't have end watt or end voltage; set them as NaN\n",
        "cooking_sessions_renamed[\"END WATT\"] = np.nan\n",
        "cooking_sessions_renamed[\"END VOLTAGE\"] = np.nan\n",
        "cooking_sessions_renamed[\"WATT DIFFERENCE\"] = np.nan\n",
        "cooking_sessions_renamed[\"VOLTAGE DIFFERENCE\"] = np.nan"
      ],
      "metadata": {
        "id": "c2bewvCJurk6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reorder Kosko columns to match A2EI order and naming\n",
        "filtered_df_standardized = filtered_df.rename(columns={\n",
        "    \"START TIME\": \"START TIME\",\n",
        "    \"END TIME\": \"END TIME\",\n",
        "    \"START WATT\": \"START WATT\",\n",
        "    \"END WATT\": \"END WATT\",\n",
        "    \"START VOLTAGE\": \"START VOLTAGE\",\n",
        "    \"END VOLTAGE\": \"END VOLTAGE\",\n",
        "    \"WATT DIFFERENCE\": \"WATT DIFFERENCE\",\n",
        "    \"VOLTAGE DIFFERENCE\": \"VOLTAGE DIFFERENCE\",\n",
        "    \"DURATION\": \"DURATION\",\n",
        "    \"DEVICE ID\": \"DEVICE ID\"\n",
        "})[[\n",
        "    \"DEVICE ID\", \"START TIME\", \"END TIME\", \"DURATION\",\n",
        "    \"START VOLTAGE\", \"END VOLTAGE\", \"VOLTAGE DIFFERENCE\",\n",
        "    \"START WATT\", \"END WATT\", \"WATT DIFFERENCE\"\n",
        "]]"
      ],
      "metadata": {
        "id": "4MP0ZUE7vKWT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assign sensor type to each dataset before merging\n",
        "filtered_df_standardized[\"Sensor type\"] = \"Kosko\"\n",
        "cooking_sessions_renamed[\"Sensor type\"] = \"A2EI\"\n",
        "\n",
        "# Align columns and include Sensor type\n",
        "cols = [\n",
        "    \"DEVICE ID\", \"START TIME\", \"END TIME\", \"DURATION\",\n",
        "    \"START VOLTAGE\", \"END VOLTAGE\", \"VOLTAGE DIFFERENCE\",\n",
        "    \"START WATT\", \"END WATT\", \"WATT DIFFERENCE\", \"Sensor type\"\n",
        "]\n",
        "filtered_df_standardized = filtered_df_standardized[cols]\n",
        "cooking_sessions_renamed = cooking_sessions_renamed[cols]\n",
        "\n",
        "# Combine both datasets\n",
        "pooled_cooking_events = pd.concat([filtered_df_standardized, cooking_sessions_renamed], ignore_index=True)"
      ],
      "metadata": {
        "id": "vuGL1UTDvMbG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display(pooled_cooking_events)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "oH_hQTfOyAK_",
        "outputId": "a56eb200-6143-4428-cf28-1ba4847ccfda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "               DEVICE ID                 START TIME  \\\n",
              "0      EM_064_2023-06-17        2022-09-30 06:09:35   \n",
              "1      EM_066_2023-06-17        2022-09-30 06:20:39   \n",
              "2      EM_074_2023-07-01        2022-10-04 07:05:57   \n",
              "3      EM_075_2023-06-17        2022-10-04 06:31:31   \n",
              "4      EM_088_2023-06-27        2023-06-09 09:36:56   \n",
              "...                  ...                        ...   \n",
              "10622               1954  2023-06-10 06:38:00+00:00   \n",
              "10623               1955  2023-03-30 07:23:42+00:00   \n",
              "10624               1955  2023-03-30 07:23:42+00:00   \n",
              "10625               1955  2023-04-11 12:15:00+00:00   \n",
              "10626               1955  2023-04-11 12:15:00+00:00   \n",
              "\n",
              "                        END TIME   DURATION  START VOLTAGE  END VOLTAGE  \\\n",
              "0            2022-09-30 06:14:48   5.216667     225.854000      229.924   \n",
              "1            2022-09-30 06:27:54   7.250000     226.197000      229.342   \n",
              "2            2022-10-04 07:11:24   5.450000     228.993000      233.668   \n",
              "3            2022-10-04 06:36:45   5.233333     232.574000      236.187   \n",
              "4            2023-06-09 09:59:57  23.016667     221.481000      223.968   \n",
              "...                          ...        ...            ...          ...   \n",
              "10622  2023-06-10 06:48:00+00:00  10.000000     179.727273          NaN   \n",
              "10623  2023-03-30 08:19:00+00:00  55.300000     208.424444          NaN   \n",
              "10624  2023-03-30 08:19:00+00:00  55.300000     208.424444          NaN   \n",
              "10625  2023-04-11 12:40:00+00:00  25.000000     197.230769          NaN   \n",
              "10626  2023-04-11 12:40:00+00:00  25.000000     197.230769          NaN   \n",
              "\n",
              "       VOLTAGE DIFFERENCE   START WATT  END WATT  WATT DIFFERENCE Sensor type  \n",
              "0                   4.070  1203.236000     1.170        -1202.066       Kosko  \n",
              "1                   3.145   920.516000     1.054         -919.462       Kosko  \n",
              "2                   4.675  1401.548000     1.253        -1400.295       Kosko  \n",
              "3                   3.613   940.402000     1.265         -939.137       Kosko  \n",
              "4                   2.487   878.478000     0.118         -878.360       Kosko  \n",
              "...                   ...          ...       ...              ...         ...  \n",
              "10622                 NaN   930.400000       NaN              NaN        A2EI  \n",
              "10623                 NaN  1298.444444       NaN              NaN        A2EI  \n",
              "10624                 NaN  1298.444444       NaN              NaN        A2EI  \n",
              "10625                 NaN  1155.984615       NaN              NaN        A2EI  \n",
              "10626                 NaN  1155.984615       NaN              NaN        A2EI  \n",
              "\n",
              "[10627 rows x 11 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DEVICE ID</th>\n",
              "      <th>START TIME</th>\n",
              "      <th>END TIME</th>\n",
              "      <th>DURATION</th>\n",
              "      <th>START VOLTAGE</th>\n",
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              "      <th>VOLTAGE DIFFERENCE</th>\n",
              "      <th>START WATT</th>\n",
              "      <th>END WATT</th>\n",
              "      <th>WATT DIFFERENCE</th>\n",
              "      <th>Sensor type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>EM_064_2023-06-17</td>\n",
              "      <td>2022-09-30 06:09:35</td>\n",
              "      <td>2022-09-30 06:14:48</td>\n",
              "      <td>5.216667</td>\n",
              "      <td>225.854000</td>\n",
              "      <td>229.924</td>\n",
              "      <td>4.070</td>\n",
              "      <td>1203.236000</td>\n",
              "      <td>1.170</td>\n",
              "      <td>-1202.066</td>\n",
              "      <td>Kosko</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>EM_066_2023-06-17</td>\n",
              "      <td>2022-09-30 06:20:39</td>\n",
              "      <td>2022-09-30 06:27:54</td>\n",
              "      <td>7.250000</td>\n",
              "      <td>226.197000</td>\n",
              "      <td>229.342</td>\n",
              "      <td>3.145</td>\n",
              "      <td>920.516000</td>\n",
              "      <td>1.054</td>\n",
              "      <td>-919.462</td>\n",
              "      <td>Kosko</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>EM_074_2023-07-01</td>\n",
              "      <td>2022-10-04 07:05:57</td>\n",
              "      <td>2022-10-04 07:11:24</td>\n",
              "      <td>5.450000</td>\n",
              "      <td>228.993000</td>\n",
              "      <td>233.668</td>\n",
              "      <td>4.675</td>\n",
              "      <td>1401.548000</td>\n",
              "      <td>1.253</td>\n",
              "      <td>-1400.295</td>\n",
              "      <td>Kosko</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>EM_075_2023-06-17</td>\n",
              "      <td>2022-10-04 06:31:31</td>\n",
              "      <td>2022-10-04 06:36:45</td>\n",
              "      <td>5.233333</td>\n",
              "      <td>232.574000</td>\n",
              "      <td>236.187</td>\n",
              "      <td>3.613</td>\n",
              "      <td>940.402000</td>\n",
              "      <td>1.265</td>\n",
              "      <td>-939.137</td>\n",
              "      <td>Kosko</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>EM_088_2023-06-27</td>\n",
              "      <td>2023-06-09 09:36:56</td>\n",
              "      <td>2023-06-09 09:59:57</td>\n",
              "      <td>23.016667</td>\n",
              "      <td>221.481000</td>\n",
              "      <td>223.968</td>\n",
              "      <td>2.487</td>\n",
              "      <td>878.478000</td>\n",
              "      <td>0.118</td>\n",
              "      <td>-878.360</td>\n",
              "      <td>Kosko</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10622</th>\n",
              "      <td>1954</td>\n",
              "      <td>2023-06-10 06:38:00+00:00</td>\n",
              "      <td>2023-06-10 06:48:00+00:00</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>179.727273</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>930.400000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A2EI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10623</th>\n",
              "      <td>1955</td>\n",
              "      <td>2023-03-30 07:23:42+00:00</td>\n",
              "      <td>2023-03-30 08:19:00+00:00</td>\n",
              "      <td>55.300000</td>\n",
              "      <td>208.424444</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1298.444444</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A2EI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10624</th>\n",
              "      <td>1955</td>\n",
              "      <td>2023-03-30 07:23:42+00:00</td>\n",
              "      <td>2023-03-30 08:19:00+00:00</td>\n",
              "      <td>55.300000</td>\n",
              "      <td>208.424444</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1298.444444</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A2EI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10625</th>\n",
              "      <td>1955</td>\n",
              "      <td>2023-04-11 12:15:00+00:00</td>\n",
              "      <td>2023-04-11 12:40:00+00:00</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>197.230769</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1155.984615</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A2EI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10626</th>\n",
              "      <td>1955</td>\n",
              "      <td>2023-04-11 12:15:00+00:00</td>\n",
              "      <td>2023-04-11 12:40:00+00:00</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>197.230769</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1155.984615</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A2EI</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10627 rows × 11 columns</p>\n",
              "</div>\n",
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              "        async function quickchart(key) {\n",
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              "        background-color: #E8F0FE;\n",
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              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
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              "      }\n",
              "\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pooled_cooking_events",
              "summary": "{\n  \"name\": \"pooled_cooking_events\",\n  \"rows\": 10627,\n  \"fields\": [\n    {\n      \"column\": \"DEVICE ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          1953,\n          1939,\n          1948\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"START TIME\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10479,\n        \"samples\": [\n          \"2023-06-10 08:11:02\",\n          \"2023-06-16 05:03:34\",\n          \"2023-06-13 09:19:02\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"END TIME\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10476,\n        \"samples\": [\n          \"2023-06-10 18:40:41\",\n          \"2023-05-28 06:35:00+00:00\",\n          \"2023-06-04 12:28:00+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DURATION\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.372247233394923,\n        \"min\": 5.0,\n        \"max\": 90.0,\n        \"num_unique_values\": 1869,\n        \"samples\": [\n          36.65,\n          8.216666666666667,\n          68.73333333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"START VOLTAGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.029856186620997,\n        \"min\": 126.64999999999999,\n        \"max\": 247.84285714285716,\n        \"num_unique_values\": 8969,\n        \"samples\": [\n          234.341,\n          196.242,\n          231.418\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"END VOLTAGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.620207277696618,\n        \"min\": 0.0,\n        \"max\": 247.245,\n        \"num_unique_values\": 7244,\n        \"samples\": [\n          219.139,\n          245.014,\n          238.317\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VOLTAGE DIFFERENCE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.869136804855994,\n        \"min\": -220.826,\n        \"max\": 52.58100000000002,\n        \"num_unique_values\": 7504,\n        \"samples\": [\n          16.717000000000013,\n          8.091999999999985,\n          5.062999999999988\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"START WATT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 145.5301455392049,\n        \"min\": 300.345,\n        \"max\": 2706.725,\n        \"num_unique_values\": 10232,\n        \"samples\": [\n          879.724,\n          987.669,\n          1313.6399999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"END WATT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 66.0013561291155,\n        \"min\": 0.0,\n        \"max\": 999.513,\n        \"num_unique_values\": 626,\n        \"samples\": [\n          0.427,\n          0.36,\n          0.176\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WATT DIFFERENCE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 107.75067022148569,\n        \"min\": -1400.295,\n        \"max\": 451.934,\n        \"num_unique_values\": 8739,\n        \"samples\": [\n          -969.321,\n          -855.724,\n          -764.1550000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sensor type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"A2EI\",\n          \"Kosko\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Comparison of outages and cooking event start times"
      ],
      "metadata": {
        "id": "0rL6xurJeb5Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Outage data based on previous Spotlight Kampala research\n",
        "# Outage percentages by hour (0–23)\n",
        "outage_values = [\n",
        "    1.883157, 3.339150, 2.648264, 3.039898, 3.079377, 3.518385,\n",
        "    5.476553, 4.263753, 5.369170, 6.795948, 5.219149, 7.945582,\n",
        "    6.060056, 5.681845, 5.043861, 4.694865, 3.422056, 2.605627,\n",
        "    5.092816, 3.790002, 3.428373, 1.618647, 2.969625, 3.013841\n",
        "]\n",
        "\n",
        "# Adjust hour range to 1–24 (to match cooking event logic)\n",
        "outage_hours = np.arange(1, 25)"
      ],
      "metadata": {
        "id": "PUy-MkSywlyA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Step 1: Prepare clean datetime ---\n",
        "plot_df = pooled_cooking_events.copy()\n",
        "plot_df[\"START TIME\"] = pd.to_datetime(plot_df[\"START TIME\"], errors=\"coerce\")\n",
        "plot_df = plot_df.dropna(subset=[\"START TIME\"])\n",
        "plot_df[\"Start hour\"] = plot_df[\"START TIME\"].dt.hour + 1\n",
        "\n",
        "# --- Step 2: Cooking event percentages ---\n",
        "hourly_counts = plot_df[\"Start hour\"].value_counts().sort_index()\n",
        "hourly_percentages = 100 * hourly_counts / hourly_counts.sum()\n",
        "hourly_percentages = hourly_percentages.reindex(np.arange(1, 25), fill_value=0)\n",
        "total_cooking_events = int(hourly_counts.sum())  # 👈 count of cooking events\n",
        "\n",
        "# --- Step 3: Outage percentages ---\n",
        "outage_series = pd.Series(outage_values, index=outage_hours)\n",
        "outage_series = outage_series.reindex(np.arange(1, 25), fill_value=0)\n",
        "\n",
        "# --- Step 4: Smooth both series ---\n",
        "cooking_smoothed = gaussian_filter1d(hourly_percentages.values, sigma=1)\n",
        "outage_smoothed = gaussian_filter1d(outage_series.values, sigma=1)\n",
        "\n",
        "# --- Step 5: Plotting ---\n",
        "x_ticks = np.arange(1, 25)\n",
        "plt.figure(figsize=(10, 5))\n",
        "\n",
        "# Area plots (semi-transparent fills)\n",
        "plt.fill_between(x_ticks, outage_smoothed, color='#5f8bcf', alpha=0.5)\n",
        "plt.fill_between(x_ticks, cooking_smoothed, color='#e3555b', alpha=0.5)\n",
        "\n",
        "# Overlay line plots\n",
        "plt.plot(x_ticks, outage_smoothed, color='#5f8bcf', linewidth=1.5, label='Outage frequency')\n",
        "plt.plot(x_ticks, cooking_smoothed, color='#e3555b', linewidth=1.5, label='Cooking events')\n",
        "\n",
        "# Annotate number of cooking events\n",
        "plt.text(\n",
        "    24, 9.5, f\"n = {total_cooking_events}\", ha='right', va='top',\n",
        "    fontsize=14, color='#e3555b'\n",
        ")\n",
        "\n",
        "# Aesthetics\n",
        "plt.xlabel(\"Hour\", fontsize=14)\n",
        "plt.ylabel(\"Percentage\", fontsize=14)\n",
        "plt.xticks(x_ticks, fontsize=14)\n",
        "plt.yticks(np.arange(0, 11, 2), labels=[f\"{i}%\" for i in np.arange(0, 11, 2)], fontsize=14)\n",
        "plt.grid(axis='y', linestyle='-', alpha=0.7)\n",
        "plt.legend(frameon=False, fontsize=14)\n",
        "plt.xlim(1, 24)\n",
        "plt.ylim(0, 10)\n",
        "plt.tight_layout()\n",
        "plt.savefig(fig_path + \"Outage_vs_Cooking_Distribution_Area.png\", dpi=500)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "AnJSneCRwY0I",
        "outputId": "df2e287e-868a-4366-e3c7-fd52365bf352"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Voltage during cooking and non-cooking"
      ],
      "metadata": {
        "id": "7Ct6tWSyvvW_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Step 1: Prepare data ---\n",
        "df = pooled_cooking_events.copy()\n",
        "df[\"START TIME\"] = pd.to_datetime(df[\"START TIME\"], errors=\"coerce\")\n",
        "df[\"END TIME\"] = pd.to_datetime(df[\"END TIME\"], errors=\"coerce\")\n",
        "df[\"START VOLTAGE\"] = pd.to_numeric(df[\"START VOLTAGE\"], errors=\"coerce\")\n",
        "df[\"END VOLTAGE\"] = pd.to_numeric(df[\"END VOLTAGE\"], errors=\"coerce\")\n",
        "df[\"Voltage diff\"] = df[\"START VOLTAGE\"] - df[\"END VOLTAGE\"]  # ON - OFF\n",
        "\n",
        "df = df.dropna(subset=[\"START TIME\", \"END TIME\", \"START VOLTAGE\", \"END VOLTAGE\"])\n",
        "df[\"Hour_start\"] = df[\"START TIME\"].dt.hour\n",
        "df[\"Hour_end\"] = df[\"END TIME\"].dt.hour\n",
        "\n",
        "# Limit data to events starting and ending between 5 and 21 hours\n",
        "valid_hours = np.arange(4, 21)\n",
        "df = df[df[\"Hour_start\"].isin(valid_hours) & df[\"Hour_end\"].isin(valid_hours)]\n",
        "\n",
        "# --- Step 2: Grouped stats ---\n",
        "on_avg = df.groupby(\"Hour_start\")[\"START VOLTAGE\"].mean()\n",
        "on_std = df.groupby(\"Hour_start\")[\"START VOLTAGE\"].std()\n",
        "off_avg = df.groupby(\"Hour_end\")[\"END VOLTAGE\"].mean()\n",
        "off_std = df.groupby(\"Hour_end\")[\"END VOLTAGE\"].std()\n",
        "\n",
        "# Voltage difference summary\n",
        "hours_full = np.arange(24)\n",
        "diff_stats = df.groupby(\"Hour_start\")[\"Voltage diff\"].agg([\"mean\", \"std\", \"count\"]).reindex(hours_full)\n",
        "\n",
        "# --- Step 3: Plotting ---\n",
        "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), sharex=True, height_ratios=[1, 1])\n",
        "\n",
        "# --- Top plot: Voltage levels ---\n",
        "ax1.plot(on_avg.index, on_avg, color=\"blue\", linewidth=2, label=\"Cooking device on\")\n",
        "ax1.plot(off_avg.index, off_avg, color=\"red\", linewidth=2, label=\"Cooking device off\")\n",
        "\n",
        "# Std deviation shading for hours 5–21 only\n",
        "on_mask = on_avg.index.isin(valid_hours)\n",
        "off_mask = off_avg.index.isin(valid_hours)\n",
        "ax1.fill_between(on_avg.index[on_mask], (on_avg - on_std)[on_mask], (on_avg + on_std)[on_mask], color=\"blue\", alpha=0.1)\n",
        "ax1.fill_between(off_avg.index[off_mask], (off_avg - off_std)[off_mask], (off_avg + off_std)[off_mask], color=\"red\", alpha=0.1)\n",
        "\n",
        "# Nominal voltage band\n",
        "nominal_voltage = 240\n",
        "lower_bound = 0.94 * nominal_voltage\n",
        "upper_bound = 1.06 * nominal_voltage\n",
        "ax1.axhline(nominal_voltage, color=\"black\", linewidth=1.2, label=\"Nominal voltage (240 V)\", zorder=-1)\n",
        "ax1.axhspan(lower_bound, upper_bound, color=\"gray\", alpha=0.15, label=\"±6% allowable voltage range\")\n",
        "\n",
        "ax1.set_ylabel(\"Average voltage (V)\", fontsize=18)\n",
        "ax1.set_ylim(175, 275)\n",
        "ax1.set_xlim(0, 23)\n",
        "ax1.set_xticks(np.arange(24))\n",
        "ax1.set_xticklabels(np.arange(1, 25), fontsize=16)\n",
        "ax1.tick_params(axis=\"both\", which=\"both\", direction=\"out\", length=6, labelsize=16)\n",
        "ax1.legend(frameon=False, fontsize=15, loc='lower left')\n",
        "\n",
        "# --- Bottom plot: Voltage difference (ON - OFF) ---\n",
        "mask_diff = diff_stats[\"count\"] >= 25\n",
        "ax2.plot(diff_stats.index[mask_diff], diff_stats[\"mean\"][mask_diff], color=\"darkgreen\", linewidth=2, label=\"Voltage difference (ON-OFF)\", zorder=1)\n",
        "ax2.fill_between(\n",
        "    diff_stats.index[mask_diff],\n",
        "    (diff_stats[\"mean\"] - diff_stats[\"std\"])[mask_diff],\n",
        "    (diff_stats[\"mean\"] + diff_stats[\"std\"])[mask_diff],\n",
        "    color=\"darkgreen\", alpha=0.15\n",
        ")\n",
        "ax2.axhline(0, color=\"black\", linestyle=\"-\", linewidth=1)\n",
        "\n",
        "ax2.set_xlabel(\"Hour\", fontsize=18)\n",
        "ax2.set_ylabel(\"Δ Voltage (V)\", fontsize=18)\n",
        "ax2.set_xlim(0, 23)\n",
        "ax2.set_ylim(-30, 30)\n",
        "ax2.set_xticks(np.arange(24))\n",
        "ax2.set_xticklabels(np.arange(1, 25), fontsize=16)\n",
        "ax2.tick_params(axis=\"both\", direction=\"out\", length=6, labelsize=16)\n",
        "ax2.legend(frameon=False, fontsize=15, loc='upper left')\n",
        "\n",
        "# --- Styling ---\n",
        "for ax in (ax1, ax2):\n",
        "    for spine in ax.spines.values():\n",
        "        spine.set_color(\"black\")\n",
        "        spine.set_linewidth(0.8)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(fig_path + \"Voltage_Event_Panel_with_Differences.png\", dpi=500)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "4PUVwfv_6smV",
        "outputId": "a0e34afa-2fb4-4d2f-faf3-e86a27589ff6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Print overall average voltage difference where count >= 25 ---\n",
        "overall_avg_diff = diff_stats.loc[mask_diff, \"mean\"].mean()\n",
        "print(f\"Average voltage difference (ON - OFF) across hours with ≥25 events: {overall_avg_diff:.2f} V\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h6TZV5_XBvOn",
        "outputId": "61e28cc5-b743-465b-81e5-c3d0c4ce5747"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average voltage difference (ON - OFF) across hours with ≥25 events: -7.31 V\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Voltage drop versus starting voltage"
      ],
      "metadata": {
        "id": "18g6nN83EiQL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Step 1: Prepare the data ---\n",
        "df = pooled_cooking_events.copy()\n",
        "df[\"START VOLTAGE\"] = pd.to_numeric(df[\"START VOLTAGE\"], errors=\"coerce\")\n",
        "df[\"END VOLTAGE\"] = pd.to_numeric(df[\"END VOLTAGE\"], errors=\"coerce\")\n",
        "df = df.dropna(subset=[\"START VOLTAGE\", \"END VOLTAGE\"])\n",
        "\n",
        "# Compute voltage difference\n",
        "df[\"VOLTAGE DIFFERENCE\"] = df[\"END VOLTAGE\"] - df[\"START VOLTAGE\"]\n",
        "voltage = df[df[\"VOLTAGE DIFFERENCE\"] >= 0]\n",
        "\n",
        "# --- Step 2: Regression and R² ---\n",
        "x = voltage[\"VOLTAGE DIFFERENCE\"]\n",
        "y = voltage[\"START VOLTAGE\"]\n",
        "slope, intercept, r_value, p_value, std_err = linregress(x, y)\n",
        "r_squared = r_value**2\n",
        "\n",
        "# Prepare regression line\n",
        "x_vals = np.linspace(0, 60, 100)\n",
        "y_vals = slope * x_vals + intercept\n",
        "\n",
        "# --- Step 3: Plot ---\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Scatter plot\n",
        "sns.scatterplot(x=x, y=y, alpha=0.6, color=\"steelblue\")\n",
        "\n",
        "# Trend line\n",
        "plt.plot(x_vals, y_vals, linestyle=\"--\", color=\"black\", linewidth=2, label=f\"Trend line ($R^2$ = {r_squared:.2f})\")\n",
        "\n",
        "# Nominal voltage line and ±6% band\n",
        "nominal_voltage = 240\n",
        "lower_bound = 0.94 * nominal_voltage\n",
        "upper_bound = 1.06 * nominal_voltage\n",
        "plt.axhline(nominal_voltage, color=\"black\", linewidth=1.5, label=\"Nominal voltage (240 V)\", zorder=-1)\n",
        "plt.axhspan(lower_bound, upper_bound, color=\"gray\", alpha=0.15, label=\"±6% voltage band\", zorder=-2)\n",
        "\n",
        "# Axis formatting\n",
        "plt.xlabel(\"Voltage difference (V)\", fontsize=16)\n",
        "plt.ylabel(\"Starting voltage (V)\", fontsize=16)\n",
        "plt.xlim(0, 60)\n",
        "plt.ylim(160, 260)\n",
        "plt.tick_params(axis=\"both\", labelsize=14)\n",
        "\n",
        "# Legend and styling\n",
        "plt.legend(frameon=False, fontsize=13, loc=\"upper right\")\n",
        "for spine in plt.gca().spines.values():\n",
        "    spine.set_edgecolor(\"black\")\n",
        "    spine.set_linewidth(0.8)\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig(fig_path + \"Voltage Drop vs Start Voltage.png\", dpi=500)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "acTEzoBvEknu",
        "outputId": "154b437a-022b-4db1-b6ce-d0a6fe497633"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the number of cooking events represented in the scatter plot\n",
        "print(f\"Number of cooking events represented in the scatter plot: {voltage.shape[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g4MfaElyRVWr",
        "outputId": "85c8f4c2-364f-4c46-d019-b0d6a77e6d8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of cooking events represented in the scatter plot: 7868\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percent of cooking events outside of regulator's voltage range"
      ],
      "metadata": {
        "id": "MJOC39rkL1xj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter for events where starting voltage is outside the ±6% range\n",
        "outside_thresholds = df[(df[\"START VOLTAGE\"] < lower_bound) | (df[\"START VOLTAGE\"] > upper_bound)]\n",
        "\n",
        "# Count the total number of events\n",
        "total_events = df.shape[0]\n",
        "\n",
        "# Count the number of events outside the thresholds\n",
        "num_outside_thresholds = outside_thresholds.shape[0]\n",
        "\n",
        "# Calculate the percentage of events outside the thresholds\n",
        "percentage_outside_thresholds = (num_outside_thresholds / total_events) * 100\n",
        "\n",
        "# Display the result\n",
        "print(f\"Percentage of cooking events with voltages outside ±6% of 240V: {percentage_outside_thresholds:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gJEXNKqrL7OW",
        "outputId": "4ed7beb0-a9cb-46d9-d774-c1c5c8a72dac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of cooking events with voltages outside ±6% of 240V: 35.19%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bgJL0DQ0UI9A"
      },
      "source": [
        "## Cooking event identification and analytics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AjskMuihtm5Y"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "def process_combined_sensor_data(df):\n",
        "    # Universal thresholds\n",
        "    POWER_THRESHOLD = 200  # Watts\n",
        "    CURRENT_THRESHOLD = 1  # Amperes\n",
        "    TIME_THRESHOLD_OFF_TO_ON = 120  # 2 minutes in seconds\n",
        "    MAX_EVENT_DURATION = 5400  # 90 minutes in seconds\n",
        "    MIN_EVENT_DURATION = 4  # Minimum duration for a cooking event in minutes\n",
        "    PRECOOKING_VOLTAGE_THRESHOLD = 300  # 5 minutes in seconds\n",
        "\n",
        "    # Convert columns to numeric where necessary\n",
        "    numeric_columns = ['Voltage', 'Current', 'Power', 'Electricity consumption']\n",
        "    df[numeric_columns] = df[numeric_columns].apply(pd.to_numeric, errors='coerce')\n",
        "    df = df.dropna(subset=numeric_columns).copy()\n",
        "\n",
        "    # Combine Date and Time to form a complete datetime object\n",
        "    df['Date'] = df['Date'].astype(str)\n",
        "    df['Time'] = df['Time'].astype(str)\n",
        "    df['Timestamp'] = pd.to_datetime(\n",
        "        df['Date'] + ' ' + df['Time'], format='%m-%d-%Y %H:%M:%S', errors='coerce'\n",
        "    )\n",
        "\n",
        "    # Drop rows with invalid Device status\n",
        "    df = df[df['Device status'].isin(['ON', 'OFF'])].copy()\n",
        "\n",
        "    # Sort DataFrame\n",
        "    df = df.sort_values(by=['Sensor ID', 'Timestamp']).reset_index(drop=True)\n",
        "\n",
        "    def identify_cooking_events(df):\n",
        "        events = []\n",
        "        grouped = df.groupby('Sensor ID')\n",
        "\n",
        "        for sensor_id, group in grouped:\n",
        "            # if group.iloc[0]['Sensor type'] == 'A2EI':\n",
        "            #     print(f\"\\nProcessing Sensor ID: {sensor_id}\")\n",
        "            group = group.reset_index(drop=True)\n",
        "            i = 0\n",
        "\n",
        "            while i < len(group):\n",
        "                on_condition = (\n",
        "                    group.loc[i, 'Device status'] == 'ON' and\n",
        "                    group.loc[i, 'Current'] >= CURRENT_THRESHOLD and\n",
        "                    group.loc[i, 'Power'] >= POWER_THRESHOLD\n",
        "                )\n",
        "\n",
        "                if on_condition:\n",
        "                    on_index = i\n",
        "                    on_time = group.loc[on_index, 'Timestamp']\n",
        "                    on_voltage = group.loc[on_index, 'Voltage']\n",
        "\n",
        "                    # Find Pre-cooking voltage (OFF event within 5 minutes before ON)\n",
        "                    off_rows_before_on = group[(group['Device status'] == 'OFF') &\n",
        "                                               (group['Timestamp'] < on_time) &\n",
        "                                               ((on_time - group['Timestamp']).dt.total_seconds() <= PRECOOKING_VOLTAGE_THRESHOLD)]\n",
        "                    pre_cooking_voltage = off_rows_before_on['Voltage'].iloc[-1] if not off_rows_before_on.empty else None\n",
        "\n",
        "                    j = on_index + 1\n",
        "                    last_valid_off = None\n",
        "\n",
        "                    while j < len(group):\n",
        "                        # if group.loc[j, 'Sensor type'] == 'A2EI':\n",
        "                        #     print(f\"\\nChecking row {j} for matching OFF or interrupting ON event...\")\n",
        "                        #     print(group.loc[j])\n",
        "\n",
        "                        off_condition = group.loc[j, 'Device status'] == 'OFF'\n",
        "                        subsequent_on_condition = (\n",
        "                            j + 1 < len(group) and\n",
        "                            group.loc[j + 1, 'Device status'] == 'ON' and\n",
        "                            (group.loc[j + 1, 'Timestamp'] - group.loc[j, 'Timestamp']).total_seconds() <= TIME_THRESHOLD_OFF_TO_ON and\n",
        "                            group.loc[j + 1, 'Current'] >= CURRENT_THRESHOLD and\n",
        "                            group.loc[j + 1, 'Power'] >= POWER_THRESHOLD\n",
        "                        )\n",
        "                        exceed_90_minutes = (\n",
        "                            group.loc[j, 'Timestamp'] - on_time\n",
        "                        ).total_seconds() > MAX_EVENT_DURATION\n",
        "\n",
        "                        if off_condition:\n",
        "                            if subsequent_on_condition:\n",
        "                                # if group.loc[j, 'Sensor type'] == 'A2EI':\n",
        "                                #     print(\"\\nIntermediate OFF event skipped due to a valid ON event within 2 minutes.\")\n",
        "                                j += 1\n",
        "                                continue\n",
        "                            else:\n",
        "                                # if group.loc[j, 'Sensor type'] == 'A2EI':\n",
        "                                #     print(\"\\nFinal matching OFF event found:\")\n",
        "                                #     print(group.loc[j])\n",
        "                                #     input(\"\\nPress Enter to calculate the event details...\")\n",
        "                                last_valid_off = j\n",
        "                                break\n",
        "                        if exceed_90_minutes:\n",
        "                            # if group.loc[j, 'Sensor type'] == 'A2EI':\n",
        "                            #     print(\"\\nExceeded 90-minute threshold. Skipping current ON event.\")\n",
        "                            break\n",
        "                        j += 1\n",
        "\n",
        "                    if last_valid_off is not None:\n",
        "                        off_time = group.loc[last_valid_off, 'Timestamp']\n",
        "                        off_voltage = group.loc[last_valid_off, 'Voltage']\n",
        "                        duration = (off_time - on_time).total_seconds() / 60\n",
        "\n",
        "                        if MIN_EVENT_DURATION <= duration <= 90:\n",
        "                            event_data = group.loc[on_index:last_valid_off]\n",
        "                            event_data = event_data[event_data['Device status'] == 'ON']\n",
        "\n",
        "                            avg_watt = event_data['Power'].mean()\n",
        "                            avg_current = event_data['Current'].mean()\n",
        "                            avg_voltage = event_data['Voltage'].mean()\n",
        "                            electricity_consumption = (\n",
        "                                group.loc[last_valid_off, 'Electricity consumption'] -\n",
        "                                group.loc[on_index, 'Electricity consumption']\n",
        "                            )\n",
        "\n",
        "                            if 0 <= electricity_consumption <= 5 and avg_watt >= POWER_THRESHOLD and avg_current >= CURRENT_THRESHOLD:\n",
        "                                # if group.loc[i, 'Sensor type'] == 'A2EI':  # Debugging only for A2EI\n",
        "                                #     print(\"\\nCalculated variables for this cooking event:\")\n",
        "                                #     print(f\"Start time: {on_time}\")\n",
        "                                #     print(f\"End time: {off_time}\")\n",
        "                                #     print(f\"Event duration: {duration:.2f} minutes\")\n",
        "                                #     print(f\"Average power: {avg_watt:.2f} W\")\n",
        "                                #     print(f\"Average current: {avg_current:.2f} A\")\n",
        "                                #     print(f\"Average voltage: {avg_voltage:.2f} V\")\n",
        "                                #     print(f\"Electricity consumption: {electricity_consumption:.3f} kWh\")\n",
        "                                #     print(f\"Sensor ID: {sensor_id}\")\n",
        "                                #     print(f\"Appliances: {group.loc[on_index, 'Appliances']}\")\n",
        "                                #     print(f\"Sensor type: {group.loc[on_index, 'Sensor type']}\")\n",
        "\n",
        "                                events.append({\n",
        "                                    'Start time': on_time,\n",
        "                                    'End time': off_time,\n",
        "                                    'Event duration': duration,\n",
        "                                    'Average power': avg_watt,\n",
        "                                    'Average current': avg_current,\n",
        "                                    'Average voltage': avg_voltage,\n",
        "                                    'Electricity consumption': electricity_consumption,\n",
        "                                    'Sensor ID': sensor_id,\n",
        "                                    'Appliances': group.loc[on_index, 'Appliances'],\n",
        "                                    'Sensor type': group.loc[on_index, 'Sensor type'],\n",
        "                                    'On voltage': on_voltage,\n",
        "                                    'Off voltage': off_voltage,\n",
        "                                    'Pre-cooking voltage': pre_cooking_voltage  # New column\n",
        "                                })\n",
        "                        i = last_valid_off + 1\n",
        "                        continue\n",
        "                    else:\n",
        "                        i += 1\n",
        "                else:\n",
        "                    i += 1\n",
        "        return pd.DataFrame(events)\n",
        "\n",
        "    return identify_cooking_events(df)\n",
        "\n",
        "# Example usage\n",
        "all_events_df = process_combined_sensor_data(combined_raw_consumption_monitoring_data)\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hcll03W_qtI3"
      },
      "source": [
        "# Perceptions of capacity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2oE0TjUffddp"
      },
      "outputs": [],
      "source": [
        "# All columns and labels, including \"Too expensive\"\n",
        "columns_all = [\n",
        "    \"ability_reasons/frequent_power_blackouts\",\n",
        "    \"ability_reasons/the_electricity_is_too_week_to\",\n",
        "    \"ability_reasons/fear_of_overloading_the_transf\",\n",
        "    \"ability_reasons/my_landlord_does_not_allow_me_\",\n",
        "    \"ability_reasons/my_ability_to_cook_with_electr\",\n",
        "    \"ability_reasons/other\",\n",
        "    \"ability_reasons/it_is_too_expensive_to_cook_wi\"\n",
        "]\n",
        "\n",
        "labels_all = [\n",
        "    \"Outages too frequent\",\n",
        "    'Voltage too low (\"too weak\")',\n",
        "    \"Transformer will overload\",\n",
        "    \"Landlord won't allow\",\n",
        "    \"No limitations\",\n",
        "    \"Other\",\n",
        "    \"Too expensive\"\n",
        "]\n",
        "\n",
        "colors_all = [\n",
        "    \"#101916\", \"#125534\", \"#61be92\", \"#fdcf5f\",\n",
        "    \"#99440c\", \"#5f8cd1\", \"#d94c5c\"\n",
        "]\n",
        "\n",
        "# Subset data\n",
        "df_temp = df_consumption_monitoring_pre_survey['perception_repeat']\n",
        "\n",
        "# Count responses\n",
        "counts = [df_temp[col].astype(bool).sum() for col in columns_all]\n",
        "\n",
        "# Sort by count\n",
        "sorted_indices = sorted(range(len(counts)), key=lambda k: counts[k], reverse=True)\n",
        "counts_sorted = [counts[i] for i in sorted_indices]\n",
        "labels_sorted = [labels_all[i] for i in sorted_indices]\n",
        "colors_sorted = [colors_all[i] for i in sorted_indices]\n",
        "\n",
        "# Calculate percentages\n",
        "total = sum(counts_sorted)\n",
        "percentages_sorted = [(c / total) * 100 for c in counts_sorted]\n",
        "\n",
        "# Create and display table\n",
        "summary_df = pd.DataFrame({\n",
        "    \"Reason\": labels_sorted,\n",
        "    \"Count\": counts_sorted,\n",
        "    \"Percentage\": [f\"{p:.1f}%\" for p in percentages_sorted]\n",
        "})\n",
        "\n",
        "display(summary_df)\n",
        "\n",
        "# Plot donut chart without labels\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "ax.pie(\n",
        "    counts_sorted,\n",
        "    labels=[\"\"] * len(counts_sorted),  # No labels\n",
        "    startangle=140,\n",
        "    wedgeprops=dict(width=0.4),\n",
        "    colors=colors_sorted\n",
        ")\n",
        "\n",
        "fig.savefig(fig_path + \"Impacts to e-Cooking Ability Including Expensive.png\", dpi=500, bbox_inches='tight')\n",
        "plt.show()\n",
        "\n",
        "# Count unique respondents who selected at least one reason\n",
        "n_respondents = (df_temp[columns_all].astype(bool).any(axis=1)).sum()\n",
        "\n",
        "print(f\"Number of respondents included in the analysis: {n_respondents}\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "toc_visible": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}